Abstract
Lung and colon cancers are two of the most common causes of death and morbidity in humans. One of the most important aspects of appropriate treatment is the histopathological diagnosis of such cancers. As a result, the main goal of this study is to use a multi-input capsule network and digital histopathology images to build an enhanced computerized diagnosis system for detecting squamous cell carcinomas and adenocarcinomas of the lungs, as well as adenocarcinomas of the colon. Two convolutional layer blocks are used in the proposed multi-input capsule network. The CLB (Convolutional Layers Block) employs traditional convolutional layers, whereas the SCLB (Separable Convolutional Layers Block) employs separable convolutional layers. The CLB block takes unprocessed histopathology images as input, whereas the SCLB block takes uniquely pre-processed histopathological images. The pre-processing method uses color balancing, gamma correction, image sharpening, and multi-scale fusion as the major processes because histopathology slide images are typically red blue. All three channels (Red, Green, and Blue) are adequately compensated during the color balancing phase. The dual-input technique aids the model’s ability to learn features more effectively. On the benchmark LC25000 dataset, the empirical analysis indicates a significant improvement in classification results. The proposed model provides cutting-edge performance in all classes, with 99.58% overall accuracy for lung and colon abnormalities based on histopathological images.
Highlights
The World Health Organization considers cancer being one of the deadliest diseases
Convolutional layers belong to convolutional neural networks (CNNs) [49], separable convolutional layers belong to depthwise separable convolutional neural networks [25] and primary capsule layers belong to capsule networks [50], we briefly describe these fundamental concepts before describing the overall architecture of the proposed model
We tested a variety of architectures in this research, but we found that using a multi-input architecture, which combines the capabilities of traditional as well as separable convolutional layers with capsule layers, yielded the best results
Summary
The World Health Organization considers cancer being one of the deadliest diseases. Lung cancer is responsible for 18.4% of cancer-related deaths and 11.6% of all cancer cases. Colon cancer accounts for 9.2% of all cancer-related fatalities worldwide [1,2,3]. There has been an increase in recent trends for malignant tumor rates, which could be attributed to an increase in population. Cancer affects people of all ages, but those between the ages of 50 and 60 are the most vulnerable. Death rates could rise by 60% by 2035 if current trends continue [4,5]
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