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Breast cancer classification with Keras and deep learning

The post on the blog will be devoted to the breast cancer classification, implemented using machine learning techniques and neural networks. Introduction to Breast Cancer The goal of the project is a medical data analysis using artificial intelligence methods such as machine learning and deep learning for classifying cancers (malignant or benign). Breast cancer is [ Breast Cancer Classification - About the Python Project. In this project in python, we'll build a classifier to train on 80% of a breast cancer histology image dataset. Of this, we'll keep 10% of the data for validation. Using Keras, we'll define a CNN (Convolutional Neural Network), call it CancerNet, and train it on our images Convolutional Neural Network (CNN) models are a type of deep learning architecture introduced to achieve the correct classification of breast cancer. This paper has a two-fold purpose. The first aim is to investigate the various deep learning models in classifying breast cancer histopathology images. This study identified the most accurate models in terms of the binary, four, and eight.

Breast cancer classification using scikit-learn and Keras

Classification for breast cancer - Keras Deep Learning Cookbook. Keras Installation. Keras Installation. Introduction. Installing Keras on Ubuntu 16.04. Installing Keras with Jupyter Notebook in a Docker image. Installing Keras on Ubuntu 16.04 with GPU enabled. Working with Keras Datasets and Models. Working with Keras Datasets and Models Intro to Keras with breast cancer data[ANN] Python notebook using data from Breast Cancer Wisconsin (Diagnostic) Data Set · 31,326 views · 3y ago · beginner, deep learning, classification, +1 more healthcar

Project in Python - Breast Cancer Classification with Deep

  1. In this workbook, I trained an ANN (Artificial Neural Network) using Keras to classify tumors into Malignant or Benign type, when provided with the tumor's dimensions. In the output we will have probability of tumor of belonging to either Malignant or Benign class. The whole project is divided into 3 parts. Data pre-processing and quick analysis
  2. In this article, I will cover the training of deep learning algorithm for binary classification of malignant/benign cases of breast cancer. This will be possible by using a trustworthy machine.
  3. Breast Cancer Classification: A Deep Learning Approach for Digital Pathology. 1. Center for Advanced Computing and Data Science (CACDS) University of Houston Houston USA. 2. Department of Electrical Engineering University of Texas at Tyler Tyler USA
  4. A novel deep learning based technique for effective cancer detection. deep-learning convolutional-neural-networks resnext xception-model capsule-network breast-cancer-classification stochastic-weight-averaging patchcamelyon grand-challenge weight-space-ensembling. Updated on May 2, 2020
  5. In this article, I will try to automate the breast cancer classification by analyzing breast histology images using various image classification techniques using PyTorch and Deep Learning
  6. Keras Deep Neural Network using Breast Cancer Data with Explanation of Predictions This model is trained on 497 training examples and is tested for accuracy on 151 different testing examples. The accuracy is about 97%
  7. Deep learning-based breast cancer classification through medical imaging modalities: state of the art and research challenges. Ghulam Murtaza 1,2, Liyana Shuib 1, Ainuddin Wahid Abdul Wahab 1, Ghulam Mujtaba 1, Ghulam Mujtaba 3, Henry Friday Nweke 1,4, Mohammed Ali Al-garadi 5, Fariha Zulfiqar 1, Ghulam Raza 6 & Nor Aniza Azmi

Breast Cancer Classification Using Deep Learning

Breast cancer is the second most common cancer in women and men worldwide. In 2012, it represented about 12 percent of all new cancer cases and 25 percent of all cancers in women. Breast cancer starts when cells in the breast begin to grow out of control. These cells usually form a tumor that can often be seen on an x-ray or felt as a lump TensorFlow 2 Tutorial: Get Started in Deep Learning With tf.keras; A Gentle Introduction to k-fold Cross-Validation; Summary. In this tutorial, you discovered how to develop a Multilayer Perceptron neural network model for the cancer survival binary classification dataset. Specifically, you learned

deep learning models have the ability to yield complicated and high-level features from images automatically [26]. Consequently, numerous recent studies employed deep learning approaches, with and without leveraging the pre-trained models, for the classification of breast cancer histopathology images Deep learning in the field of healthcare is used to identify patterns, classify and segment medical images. As with most image related tasks, convolutional neural networks are used to do this. The classification problem tackled here is to classify histopathology slides of Invasive Ductal Carcinoma (IDC) as either malignant or benign BCNet: A Deep Convolutional Neural Network for Breast Cancer Grading. 07/11/2021 ∙ by Pouya Hallaj Zavareh, et al. ∙ 70 ∙ share . Breast cancer has become one of the most prevalent cancers by which people all over the world are affected and is posed serious threats to human beings, in a particular woman Multiple Instance Learning (MIL) provides an elegant framework to deal with weakly supervised learning. In comparison with strong (i.e., fully-labeled) supervised learning where every training instance is assigned a discrete or real-valued label, the rationale of MIL paradigm is that instances are naturally grouped in labeled bags, without the need that all the instances of each bag have. Welcome back to DataFlair Keras Tutorial series. In this Keras tutorial, we will walk through deep learning with keras and an important deep learning algorithm used in keras. We will study the applications of this algorithm and also its implementation in Keras. Deep Learning is a subset of machine learning which concerns the algorithms inspired by the architecture of the brain

Classification for breast cancer - Keras Deep Learning

Breast cancer classification with Keras and Deep Learning February 18, 2019 In this tutorial, you will learn how to train a Keras deep learning model to predict breast cancer in breast histology images Breast cancer is one of the largest causes of women's death in the world today. Advance engineering of natural image classification techniques and Artificial Intelligence methods has largely been used for the breast-image classification task. The involvement of digital image classification allows the doctor and the physicians a second opinion, and it saves the doctors’ and physicians.

Intro to Keras with breast cancer data[ANN] Kaggl

  1. Breast-cancer-classification. Breast Cancer Classification using CNN and transfer learning. Citing. If you find this code useful in your research, please consider citing the blog: @misc{sagarconvolutional, Author = {Abhinav Sagar}, Title = {Convolutional Neural Network for Breast Cancer Classification}, Year = {2019}, Journal = {Towards Data.
  2. The current method for detecting breast cancer is a mammogram which is an X-ray breast tissue that is used for predictions. While further researching, I discovered a very well-documented project about Breast Cancer in Python, using Keras and this project helped me better understand the dataset and how to use it. This project can be found here
  3. Automated breast cancer multi-classification from histopathological images plays a key role in computer-aided breast cancer diagnosis or prognosis. Breast cancer multi-classification is to identify subordinate classes of breast cancer (Ductal carcinoma, Fibroadenoma, Lobular carcinoma, etc.). Howeve
  4. Multi-Class Breast Cancer Classification using Deep Learning Convolutional Neural Network Majid Nawaz, Adel A. Sewissy, Taysir Hassan A. Soliman Faculty of Computer and Information, Assiut University Abstract—Breast cancer continues to be among the leading causes of death for women and much effort has been expended i
  5. Purpose: Mammographic breast density is an established risk marker for breast cancer and is visually assessed by radiologists in routine mammogram image reading, using four qualitative Breast Imaging and Reporting Data System (BI-RADS) breast density categories. It is particularly difficult for radiologists to consistently distinguish the two most common and most variably assigned BI-RADS.
  6. CNN is a deep learning model that derives an image's features and practices these features to analyze an image. Other classification algorithm demands to remove the element of an illustration applying feature extraction algorithm. In our study, we have two groups which we need to classify 1.benign and 2.malignant

Breast Cancer Classification in Keras using ANN Kaggl

This research aims to increase the accuracy of the classification of breast cancer images by utilizing a patch-based classifier (PBC) along with deep learning architecture. The proposed system consists of a deep convolutional neural network that helps in enhancing and increasing the accuracy of the classification process World Health Organization (WHO), the number of cancer cases expected in 2025 will be 19.3 million cases. In Egypt, cancer is an increasing problem and especially breast cancer. HowtocitethisarticleRagab DA, Sharkas M, Marshall S, Ren J. 2019. Breast cancer detection using deep convolutional neural networks and support vector machines In this thesis, we implemented transfer learning from pre-trained deep neural networks ResNet18, Inception-V3Net, and ShuffleNet in terms of binary classification and multiclass classification for breast cancer from histopathological images Deep learning-based model for breast cancer histopathology image classification Abstract: Breast cancer is the most commonly found cancer in India and the world. As per Global Cancer Observatory, in 2020 this cancer alone was the reason for the death of more than 6.8 million women throughout the world

Training Neural Networks for binary classification

  1. Breast cancer is one of the largest causes of women's death in the world today. Advance engineering of natural image classification techniques and Artificial Intelligence methods has largely been used for the breast-image classification task. The involvement of digital image classification allows the doctor and the physicians a second opinion, and it saves the doctors' and physicians.
  2. The task of accurately identifying and categorizing breast cancer subtypes is a crucial clinical task, which can take hours for trained pathologists to complete. So, we will try to automate breast cancer classification by analyzing breast histology images, using image classification, PyTorch and deep learning
  3. With the recent advances in the deep learning field, the use of deep convolutional neural networks (DCNNs) in biomedical image processing becomes very encouraging. This paper presents a new classification model for breast cancer masses based on DCNNs. We investigated the use of transfer learning from AlexNet and GoogleNet pre-trained models to suit this task

In this work, four hundred different biopsy images were collected from the Bio-imaging 2018 breast histology classification challenge by participating Grand Challenge on breast cancer histology images (BACH) for multiclass breast cancer histology image classification using deep learning technique vi Table of Contents List of Figures viii List of Tables xii List of Acronyms and Abbreviations xiv 1 Introduction 1 1.1 Cancer and its Impact 1 Breast Cancer1.2 2 1.3 Computer-Aided Cancer Detection 3 1.4 Structure of the Thesis 6 2 Literature Survey 7 2.1 Traditional Approaches to Medical Image Classification 7 2.2 Deep Learning Approaches to Medical Image Classification Breast cancer is one of the leading cancer-related death causes worldwide, specially for women. In this talk, we will talk about how Deep Learning & Python could help pathologists to classify breast cancer microscopic images. The speaker will share his approach that won the ICIAR 2018 Grand Challenge on Breast Cancer Histology images, which was implemented.. Link to UCI Machine Learning Repository (where I got the dataset) - https://archive.ics.uci.edu/ml/datasets/Breast+Cancer+Wisconsin+%28Diagnostic%29Link to G..

Breast Cancer Classification: A Deep Learning Approach for

In this tutorial we are going to see, Top 10 deep learning projects. Let's get started. 1) Breast Cancer Classification. As we all know cancer is a dangerous disease and it must be detected as soon as possible. It is possible to detect cancer using histopathology images. As cancer cells are different from the regular cells. What is keras. Novel breast cancer classification framework based on deep learning ISSN 1751-9659 Received on 25th January 2020 Revised 10th May 2020 Accepted on 30th June 2020 E-First on 5th October 2020 doi: 10.1049/iet-ipr.2020.0122 www.ietdl.org Wessam M. Salama1, Azza M. Elbagoury1, Moustafa H. Aly

Breast cancer classification with Keras and Deep Learning

breast-cancer-classification · GitHub Topics · GitHu

This project will help the farmers financially as the Accepted : 12 June 2021 production increases. Published : 20 June 2021 Keywords: Image Classification, Convolutional Neural Network, Keras, Deep Learning. I. INTRODUCTION silkworms Deep Learning Algorithems for Breast Cancer Image Classification. T. Sathyapriya. Research Scholar, Department of Computer Science. Vivekanandha College of Arts and Science for Women, (Autonomous), Namakkal,India. Abstract - Breast Cancer is one of the most major reasons for death among ladies between the age of 30 to 45

Breast Cancer Classification With PyTorch and Deep Learnin

21 Apr 2018 | Python Keras Deep Learning 분류 과업(classification task)은 머신러닝에서 예측하고자 하는 변수(y)가 카테고리 속성을 가질 때(categorical)를 일컫는다. Breast cancer 데이터 셋 가져오기. American Cancer Society. Breast Cancer Facts & Figures 2017-2018. Atlanta: American Cancer Society, Inc. 2017; Lotter, William, et al. Robust breast cancer detection in mammography and digital breast tomosynthesis using annotation-efficient deep learning approach. arXiv preprint arXiv:1912.11027 (2019) Introduction. Breast cancer is the most common malignant tumor in women in China (1, 2).Breast ultrasound is more suitable for tumor discovery in Asian women considering the higher breast density (3, 4) and the younger age at diagnosis (5, 6).Patients with Breast Imaging Reporting and Data System (BI-RADS) 4a or higher findings are usually recommended to undergo core needle biopsy or surgery The updated AJCC breast cancer staging guidelines have made determining the stage of a cancer a more complicated but accurate process. So, the characteristics of each stage below are somewhat generalized. A note about staging: The American Cancer Society (ACS) and the National Cancer Institute (NCI) both say that a cancer's stage doesn't. Note: to modify the learning rate, you can import Adam optimizer from keras.optimizers package, and then compile the model with optimizer=Adam(lr=0.0005) parameter. Testing the Model Now to evaluate our model, we need to load the optimal weights via model.load_weights() method, you need to choose the weights that has the least loss value, in my.

GitHub - mark-watson/cancer-deep-learning-model: Keras

  1. ation. Then, pathologists evaluate the extent of any abnormal.
  2. In this post, you will learn about how to train an optimal neural network using Learning Curves and Python Keras. As a data scientist, it is good to understand the concepts of learning curve vis-a-vis neural network classification model to select the most optimal configuration of neural network for training high-performance neural network.. In this post, the following topics have been covered
  3. Keras Deep Learning Cookbook. $34.99 Print + eBook Buy; $27.99 eBook version Buy; More info. 1. Image classification using Keras functional APIs; 3. Data Preprocessing, Optimization, and Visualization. Classification for breast cancer; Classification for spam detection; 5
  4. in breast cancer images ([1]). In agreement with this, four deep learning network architectures including GoogLeNet, AlexNet, VGG16 deep network ([58]) and ConvNet with 3, 4, and 6 layers ([13]) were recently applied to identify breast cancer. The best example of using automated CAD system is a study conducted by Esteva and colleague o

Coudray, N. et al. Classification and mutation prediction from non-small cell lung cancer histopathology images using deep learning. Nat. Med. 24 , 1559-1567 (2018) Breast cancer is associated with the highest morbidity rates for cancer diagnoses in the world and has become a major public health issue. Early diagnosis can increase the chance of successful treatment and survival. However, it is a very challenging and time-consuming task that relies on the experience of pathologists. The automatic diagnosis of breast cancer by analyzing histopathological.

Brain Tumor Classification Using Deep Cnn Features Via

Deep learning-based breast cancer classification through

Author(s): Sanku Vishnu Darshan A-Z explanation of the usage of Timeseries Data for forecasting Photo by Icons8 team on Unsplash Hello, everyone. I welcome you to the Beginner's Series in Deep Learning with TensorFlow and Keras. This guide will help you understand the basics of TimeSeries.. In this article, we will work on Text Classification using the IMDB movie review dataset. This dataset has 50k reviews of different movies. It is a benchmark dataset used in text-classification to train and test the Machine Learning and Deep Learning model. We will create a model to predict if the movie review is positive or negative The usefulness of 3D deep learning‐based classification of breast cancer and malignancy localization from MRI has been reported. This work can potentially be very useful in the clinical domain and aid radiologists in breast cancer diagnosis

Image classification is a fascinating deep learning project. Specifically, image classification comes under the computer vision project category. In this project, we will build a convolution neural network in Keras with python on a CIFAR-10 dataset. First, we will explore our dataset, and then we will train our neural network using python and. In this tutorial, you will learn how to train a Keras deep learning model to predict breast cancer in breast histology images. Defaults to None.If None, it will be inferred from the data. This project is maintained by suraj-deshmukh Note: Multi-label classification is a type of classification in which an object can be categorized into more than. In this post, I'm going to go over a code piece for both classification and regression, varying between Keras, XGBoost, LightGBM and Scikit-Learn. There is a GitHub available with a colab button , where you instantly can run the same code, which I used in this post

There are a total of 155 images of positive patients of brain tumor and 98 images of other patients having no brain tumor. All the images are of 240X240 pixels. Brain Tumor Classification Model. First, we need to enable the GPU. To do so go to 'Runtime' in Google Colab and then click on 'Change runtime type' and select GPU Breast Cancer Classification: Using Deep Learning. Share. Flip. Like. analyticsvidhya.com - mrinal41 • 5h. ArticleVideo Book This article was published as a part of the Data Science Blogathon Image by National Cancer Institute from Unsplash. Problem . Read more on analyticsvidhya.com

Deep Learning - Classification Example Dev Skro

  1. / Breast cancer classification with Keras and Deep Learning. Latest Python Resources (check out PyQuant Books) Breast cancer classification with Keras and Deep Learning pyimagesearch.com. Published March 3, 2019 under Neural Networks. Computer Vision, HealthTech, Keras. Primary Sidebar
  2. In this paper, we present a deep learning approach based on a Convolutional Neural Network (CNN) model for multi-class breast cancer classification. The proposed approach aims to classify the breast tumors in non-just benign or malignant but we predict the subclass of the tumors like Fibroadenoma, Lobular carcinoma, etc. Experimental results on.
  3. A Deep Learning Framework for Multi-Class Classification. In our study, we proposed a deep learning approach designed for the analysis of breast cancer. The research protocol was approved by the Ethics Committee of Peking Union Medical College Hospital (PUMCH). Informed consent was waived because the data are anonymized according to the protocol
Brain Tumor Detection Using Cnn Github - Brain Tumor Cancer

Classification of Breast Cancer Histology using Deep Learnin

Conventional Machine Learning and Deep Learning Approach for Multi-Classification of Breast Cancer Histopathology Images-a Comparative Insight. Sharma S(1), Mehra R(2). Author information: (1)ECE Department, NITTTR, Chandigarh, 160019, India. shallu.ece@nitttrchd.ac.in. (2)ECE Department, NITTTR, Chandigarh, 160019, India Breast Cancer is a major cause of death worldwide among women. Hematoxylin and Eosin (H&E) stained breast tissue samples from biopsies are observed under microscopes for the primary diagnosis of breast cancer. In this paper, we propose a deep learning-based method for classification of H&E stained breast tissue images released for BACH challenge 2018 by fine-tuning Inception-v3 convolutional. In , the author proposed a structured deep learning model for solving the subordinates of breast cancer, with the best classification result reaching 92.19%. In [ 16 ], the authors proposed that hybrid CNN unit could make full use of the local and global features of an image, so as to make a more accurate prediction Artificial Neural Network (ANN) implementation on Breast Cancer Wisconsin Data Set using Python (keras) Dataset. About Breast Cancer Wisconsin (Diagnostic) Data Set Features are computed from a digitized image of a fine needle aspirate (FNA) of a breast mass A deep learning algorithm trained on a linked data set of mammograms and electronic health records achieved breast cancer detection accuracy comparable to radiologists as defined by the Breast Cancer Surveillance Consortium benchmark for screening digital mammography and revealed additional clinical risk features

3 Ways to Encode Categorical Variables for Deep Learnin

There have been several empirical studies addressing breast cancer using machine learning and soft computing techniques. Many claim that their algorithms are faster, easier, or more accurate than others are. This study is based on genetic programming and machine learning algorithms that aim to construct a system to accurately differentiate between benign and malignant breast tumors LightGBM is a gradient boosting framework that uses tree-based learning algorithms. It is designed to be distributed and efficient as compared to other boosting algorithms. Binary Classification using the Breast Cancer Dataset Human Activity Classification on the selfBACK Data Set with pycaret and keras. Peijin Chen in Towards Data Science Keywords: Deep Learning, Image Processing, Breast Cancer. 1 Introduction Breast cancer is a serious global health problem due to its morbidity and mortality, with approximately 1.5 million new cases each year. Also, is the most frequent cancer in women worldwide, with 16% of the total . Mammography has proven to be the most effective method for.

CIFAR-10 classification using Keras Tutorial. The CIFAR-10 dataset consists of 60000 32×32 colour images in 10 classes, with 6000 images per class. There are 50000 training images and 10000 test images. Recognizing photos from the cifar-10 collection is one of the most common problems in the today's world of machine learning MNIST[17], and Wisconsin Diagnostic Breast Cancer (WDBC)[16] classification. We use the Adam[8] optimization algorithm for learn-ing the network weight parameters. 2 METHODOLOGY 2.1 Machine Intelligence Library Keras[4] with Google TensorFlow[1] backend was used to imple-ment the deep learning algorithms in this study, with the aid o (1) Breast cancer pathological image classification based on artificial feature extraction and traditional machine learning algorithms: Kowal et al. used different nuclei segmentation algorithms [], the accuracy of identifying 500 breast cancer pathological images was from 96% to 100%.Zhang et al. proposed a single kernel principal component analysis method based on artificial design features. Keywords:Machine learning, deep learning, transfer learning, ensemble learning, resnet, mobilenet, densenet, pyTorch, breast cancer classification. Abstract:Aims: Early detection of breast cancer has reduced many deaths. Earlier CAD systems used to be the second opinion for radiologists and clinicians

Breast cancer is one of the foremost reasons of death among women in the world. It has the largest mortality rate compared to the types of cancer accounting for 1.9 million per year in 2020. An early diagnosis may increase the survival rates. To this end, automating the analysis and the diagnosis allows to improve the accuracy and to reduce processing.. Breast Cancer Image Classification using CNN (TensorFlow - Python) I need help to complete my code that classifies Breast Cancer Images using CNN. I need the model to predict from random images, but it keeps giving a predicting one class (Invasive) for all images which are 306 in total. In addition, I need to calculate the accuracy and.

Illustration of data augmentation applied on the Minority

python advanced project in machine learning (deep learning project with tensorflow) training testing the network using tensorflow and keras : deep learning with tensor flow and keras code : final python quiz : machine learning assignment : live classes machine learning live project : capstone projects breast cancer prediction logistic. Breast cancer is the most common cancer for women in the world [1]. Deep learning has been a tremendous success in computer vision and also has some applications in medical imaging. We trained and evaluated several convolutional neural networks for mammogram classification and tumor detection. We used the MIAS database which contains 322 mammograms Breast cancer causes hundreds of thousands of deaths each year worldwide. The early stage diagnosis and treatment can significantly reduce the mortality rate. However, the traditional manual diagnosis needs intense workload, and diagnostic errors are prone to happen with the prolonged work of pathologists. Automatic histopathology image recognition plays a key role in speeding up diagnosis and. Deep learning algorithms improve diagnostic performance of breast ultrasound. Download PDF Copy. Apr 5 2021. Ultrasound is widely used to detect breast cancer early, but misdiagnosis of benign. 1 INTRODUCTION. Cancer is a worldwide public problem. Among cancer cases in women, breast cancer has the highest incidence [].According to the statistics from the American Cancer Society, by 2020, there will be about 276,480 new cases of breast cancer in women, accounting for 30% of new cases of cancer in women [].If it can be detected early in the onset of breast cancer, the patient's five.

Fashion MNIST classification with keras and deep learning in python? How to detect Credit card fraud transaction using deep neural networks from keras in python? How to predict breast cancer using Multi Layer Perceptron from sklearn in python? How to build regression model using keras for predicting students weights in python? How to build a. Mlyahilu, J. , Kim, Y. and Kim, J. (2019) Classification of 3D Film Patterns with Deep Learning. Journal of Computer and Communications, 7, 158-165. doi: 10.4236/jcc. Breast cancer is the most frequent in females. Mammography has proven to be the most effective method for the early detection of this type of cancer. Mammographic images are sometimes difficult to understand, due to the nature of the anomalies, the low contrast image and the composition of the mammary tissues, as well as various technological factors such as spatial resolution of the image or.

Convolutional Neural Network for Breast Cancer Classificatio

Breast cancer is one of the main causes of cancer death worldwide. The diagnosis of biopsy tissue with hematoxylin and eosin stained images is non-trivial and specialists often disagree on the final diagnosis. Computer-aided Diagnosis systems contribute to reduce the cost and increase the efficiency of this process. Conventional classification approaches rely on feature extraction methods. Deep Neural Networks Improve Radiologists' Performance in Breast Cancer Screening. nyukat/breast_cancer_classifier • • 20 Mar 2019. We present a deep convolutional neural network for breast cancer screening exam classification, trained and evaluated on over 200, 000 exams (over 1, 000, 000 images) Many machine learning algorithms have been used in various medical image analysis and bioinformatics applications areas such as breast cancer, ovarian cancer, lymphoma, cervical cancer, leukemia, lung cancer, brain cancer for their prediction classification and diagnosis . Despite so much work done in this area, there is a need for an efficient.

Python: Neural Networks - AlbGriShallu SHARMA | Research Scholar | PhD | Electronics

Introduction. Deep Learning has been the most revolutionary branch of machine learning in recent years due to its amazing results. In this article, we will let you know some interesting machine learning projects in python with code in Github. You can either fork these projects and make improvements to it or you can take inspiration to develop your own deep learning projects from scratch Background: One of the most prevalent diseases these days is breast cancer which is common amongst women. This sickness has been increasing to an alarming. The approach proposed in this paper is applied to the 4-class classification of breast cancer histology images and achieves 95% accuracy on the initial test set and 88.89% accuracy on the overall. Deep Learning for Cancer Diagnosis: A Bright Future. Cancer is a leading cause of death and affects millions of lives every year. Its early detection could help to increase the survival of many lives 1 in addition to saving billions of dollars. 2 Most of the healthcare data are obtained from 'omics' (such as genomics, transcriptomics.