Histopathologic Cancer Detection Using Convolutional Neural Networks

Mrunal Chipre¹ , Harshavardhan Bhojakar² , Sachin Ghanteppagol³ , Aditya Patil⁴ , Bahubali M. Akiwate⁵

Section:Research Paper, Product Type: Journal-Paper
Vol.8 , Issue.5 , pp.43-46, Oct-2020

Online published on Oct 31, 2020

Copyright © Mrunal Chipre, Harshavardhan Bhojakar, Sachin Ghanteppagol, Aditya Patil, Bahubali M. Akiwate . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
 

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Abstract :
Lung cancer affects one out of ten humans globally. It is diagnosed by detecting the malignancy of the cells of lymph node tissue. Modern medical image processing techniques work on histopathology images, which are captured by a microscope and then analysed by using different algorithms and techniques. Machine learning algorithms are used for processing medical imaging and pathological tools. Manual detection of a cancer cell is a tedious task and involves human error, and hence computer-aided mechanisms are applied to obtain better results as compared with manual pathological detection systems. In deep learning, this is done with convolutional neural network (CNN) by learning features and then categorizing using a fully connected network. Deep learning is generally utilized in the medical imaging field, as it does not require prior expertise in a related field. In this paper, we have trained a convolutional neural network and obtained a prediction accuracy of up to 98%.

Key-Words / Index Term :
Machine Learning Algorithms, Computer-aided mechanisms, Pathology, Deep Learning, and Convolutional Neural Network

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