Diagnosis of Lung and Colon Cancer based on Clinical Pathology Images using Convolutional Neural Network and CLAHE Framework

Diagnosis of Lung and Colon Cancer based on Clinical Pathology Images using Convolutional Neural Network and CLAHE Framework

Cancer is a non-contagious disease that is the leading cause of death globally. The most common types of cancer with high mortality are lung and colon cancer. One of the efforts to reduce cases of death is early diagnosis followed by medical therapy. Tissue sampling and clinical pathological examination are the gold standard in cancer diagnosis. However, in some cases, pathological examination of tissue to the cell level requires high accuracy, depending on the contrast of the pathological image, and the experience of the clinician. Therefore, we need an image processing approach combined with artificial intelligence for automatic classification. In this study, a method is proposed for automatic classification of lung and colon cancer based on a deep learning approach. The object of the image that is classified is the histopathological image of normal tissue, benign cancer, and malignant cancer. Convolutional neural network (CNN) with VGG16 architecture and Contrast Limited Adaptive Histogram Equalization (CLAHE) were employed for demonstration of classification on 25000 histopathological images. The simulation results show that the proposed method is able to classify with a maximum accuracy of 98.96%. The system performance using CLAHE shows a higher detection accuracy than without using CLAHE and is consistent for all epoch scenarios. The comparative study shows that the proposed method outperforms some previous studies. With this proposed method, it is hoped that it can help clinicians in diagnosing cancer automatically, with low cost, high accuracy, and fast processing on large datasets.