Abstract:

Cancer is a lethal condition brought on by a confluence of hereditary disorders and several metabolic anomalies. Two of the most common causes of mortality and dysfunction in people today are lung and colon cancer. The most crucial factor in choosing the optimal course of action is typically the histological diagnosis of such cancers. This paper suggests a deep learning method for employing the Convolutional Neural Network (CNN) algorithm to identify lung cancer from medical photos. A sizable dataset of lung imaging data is used to train the CNN to identify the characteristics of cancer. Using a different set of photos, the trained model is tested to see how well it can spot malignant areas. The suggested method successfully detects lung cancer with high accuracy, sensitivity, and specificity, suggesting that it has the potential to help radiologists with early diagnosis and treatment planning. Basically, the proposed CNN algorithm detects the sub-types of cancers in both the lung and colon with higher accuracy. So that there is a chance for early diagnosis which can prevent the overall death rate.

Description:

CNN, Histological Diagnosis