Detection of Subtypes of Lung and Colon Cancer Using CNN
Author(s):
Pasupuleti Narasimha, Javvaji Likhith Chowdary, Jonnalagadda Vijay Kumar, Korlakunta Trivenu, Sk.Mulla Almas Khan
Keywords:
CNN, Histological Diagnosis, Lung Cancer, Colon Cancer, Deep Learning, Early Diagnosis
Abstract
A combination of many metabolic abnormalities and inherited illnesses can lead to the deadly disease known as cancer. Lung and colon cancer are two of the most prevalent causes of death and dysfunction among people in today's world. The Histological Diagnosis of these tumors is usually the most important element in determining the best course of treatment. This research proposes a Deep Learning approach to diagnose Lung Cancer and Colon Cancer from medical pictures using the Convolutional Neural Network (CNN) algorithm. CNN is trained on a large dataset of lung imaging data in order to recognize the features of malignancy. The trained model is evaluated to determine how effectively it can identify cancerous regions using an alternative set of images. The recommended technique successfully identifies lung cancer with high sensitivity, specificity, and accuracy, indicating that radiologists may find it useful for Early Diagnosis and treatment planning. In essence, the suggested CNN algorithm more accurately identifies the subtypes of cancer in the colon and lung. in order to increase the likelihood of an early diagnosis, which can lower the total death rate.
Article Details
Unique Paper ID: 161688

Publication Volume & Issue: Volume 10, Issue 5

Page(s): 400 - 404
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