A Deep Neural Network Approach on Spleen Cancer Detection using DenseNet-201

Abstract

Abstract: Spleen is the largest secondary lymphoid organ and play a crucial role in the regulation of innate and adaptive immune. Among all cancers, spleen cancer is one of the serious diseases which effects minor population across the globe but is potentially fatal, especially if diagnosed in a later stage of development with a 20% survival rate at 6 months. Given the intricacy of the problem, several computer-aided diagnostic methods have been proposed and developed to increase the survival rate Spleen Cancer is a malignancy of white blood cells involving tumour deposits in the spleen. Most splenic cancer do not start in the spleen, and those that do, are almost always lymphomas. Lymphoma is a type of blood cancer that develops in the lymphatic system. Although there are many systems available with the medical industry, but this research is proposed to improve the performance of existing system by employing deep learning feature of Artificial Intelligence (AI) using Convolution Neural Networks (CNN), where Convolution Neural Networks (CNN) has been implemented using DenseNet-201. This research accomplished the desired parameters with values to achieve 99.60% accuracy and 0.1240 % loss while training and testing the model. The research has been supported with the datasets from Kaggle, IEEE transactions on information technology in biomedicine and IEEE International Symposium on Biomedical Imaging. The data set contains three clinical types spleen cancer, such as CLL (chronic lymphocytic leukemia); FL (follicular lymphoma); MCL (mantle cell lymphoma).