Convolution Neural Networks for diagnosing colon and lung cancer histopathological images

09/08/2020

∙

Lung and Colon cancer are one of the leading causes of mortality and morbidity in adults. Histopathological diagnosis is one of the key components to discern cancer type. The aim of the present research is to propose a computer aided diagnosis system for diagnosing squamous cell carcinomas and adenocarcinomas of lung as well as adenocarcinomas of colon using convolutional neural networks by evaluating the digital pathology images for these cancers. Hereby, rendering artificial intelligence as useful technology in the near future. A total of 2500 digital images were acquired from LC25000 dataset containing 5000 images for each class. A shallow neural network architecture was used classify the histopathological slides into squamous cell carcinomas, adenocarcinomas and benign for the lung. Similar model was used to classify adenocarcinomas and benign for colon. The diagnostic accuracy of more than 97 and 96

READ FULL TEXT

Convolution Neural Networks for diagnosing colon and lung cancer histopathological images

Using Apple Machine Learning Algorithms to Detect and Subclassify Non-Small Cell Lung Cancer

Early Diagnosis of Lung Cancer Using Computer Aided Detection via Lung Segmentation Approach

Classification of Histopathology Images of Lung Cancer Using Convolutional Neural Network (CNN)

Representation Learning of Histopathology Images using Graph Neural Networks

NephroNet: A Novel Program for Identifying Renal Cell Carcinoma and Generating Synthetic Training Images with Convolutional Neural Networks and Diffusion Models

Applied Computer Vision on 2-Dimensional Lung X-Ray Images for Assisted Medical Diagnosis of Pneumonia

Integration of Convolutional Neural Networks for Pulmonary Nodule Malignancy Assessment in a Lung Cancer Classification Pipeline

Convolution Neural Networks for diagnosing colon and lung cancer histopathological images

Related Research

Using Apple Machine Learning Algorithms to Detect and Subclassify Non-Small Cell Lung Cancer

Early Diagnosis of Lung Cancer Using Computer Aided Detection via Lung Segmentation Approach

Classification of Histopathology Images of Lung Cancer Using Convolutional Neural Network (CNN)

Representation Learning of Histopathology Images using Graph Neural Networks

NephroNet: A Novel Program for Identifying Renal Cell Carcinoma and Generating Synthetic Training Images with Convolutional Neural Networks and Diffusion Models

Applied Computer Vision on 2-Dimensional Lung X-Ray Images for Assisted Medical Diagnosis of Pneumonia

Integration of Convolutional Neural Networks for Pulmonary Nodule Malignancy Assessment in a Lung Cancer Classification Pipeline