Multi-Cancer Detection Using Deep Learning Techniques

Abstract

Cancer is one of the main causes of death for people worldwide. Breast, lung, colon, brain and lymphoma are some of the most common types of cancer. Successful treatment can significantly increase the chances of survival. Enhancing the probability of a successful cancer treatment requires initial identification and treatment. In this paper a model is proposed using denset121 pretrained model with modified dense net block and softmax function as output layer. There are two subgroups of the total number of diseases: task 1 and task 2. Task1 include breast, kidney, cervical, leukemia while task2 include lung, oral, lymphoma, brain.A person suffering from the disease of task 1 may also suffer from a disease belonging to task 2. This model is examined using a dataset with multiple cancers, which is publicly available on Kaggle. The suggested method performs with an accuracy of 99.31% for task 1 as well as 97.02% for task 2, respectively, when analyzed alongside the most recent techniques.