The third most commonly diagnosed cancer behind breast and lung cancers is colorectal cancer. Specifically, in the minimization of health inequalities, it can be supported by the clinical care of AI guidance. To develop generalizable deep learning approaches, an enormous amount of data is essential. In this paper, cycleGAN is used to do data augmentation supposed to overcome the issue of data imbalance. Moreover, segmentation and classification of colorectal cancers are proposed.

Lung and colon cancers are dangerous diseases that can grow in organs and create a negative impact on human life in certain cases. The histological detection of such malignancies is one of the most critical parts of optimal treatment. As a result, the important objective of this article is to create an effective computerized diagnosis system for identifying adenocarcinomas of the colon as well as, adenocarcinomas and squamous cell carcinomas of the lungs using digital histopathology images and the combination of deep and machine learning techniques. For this, an effective optimized hybrid deep and machine learning framework is developed. This framework consists of two stages. In the first stage, the features of lung and colon images are extracted by principle component analysis network. Then the effective classification is conducted based on extreme learning machine (ELM) with the rider optimization algorithm which classifies lung and colon cancer into five types. The empirical investigation shows that the classification results on the benchmark LC25000 dataset have improved significantly. The use of this model will aid medical professionals in the development of an automatic and reliable system for detecting various forms of lung and colon cancers.

Colorectal image classification is a novel application area in medical image processing. Colorectal images are one of the most prevalent malignant tumour disease type in the world. However, due to the complexity of histopathological imaging, the most accurate and effective classification still needs to be addressed. In this work we proposed a novel architecture of convolution neural network with deep learning models for the multiclass classification of histopathology images. We achieved the findings using three deep learning models, including the vgg16 with 96.16% and a modified version of Resnet50 with 97.08%, however the proposed Adaptive Resnet152 model generated the best accuracy of 98.38%. The colorectal image multiclass dataset is publicly available which has 5000 images with 8 classes. In this study we have increased all classes equally, total 15000 images have been generated using image augmentation technique. This dataset consists of 60% training images and 40% testing images. The suggested method in this paper produced better results than the existing histopathology image categorization methods with the lowest error rate. For histopathological image categorization, it is a straightforward, effective, and efficient method. We were able to attain state-of-the-art outcomes by efficiently utilizing the resourced dataset.

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In the last four decades, medicine and healthcare have made revolutionary advances. During this time, the true causes of many diseases were discovered and new diagnostic procedures were devised and new remedies were invented. Globally, cancer is one of the serious diseases, which has become a widespread medical issue. A credible and early finding is especially important to reduce the risk of death. In any way, it is a difficult task that relies on the expertise of histopathologists. If a histologist is unprepared, a patient’s life may be put in danger. Deep learning has gotten a lot of attention recently and is being used in medical imaging analysis. Artificial Intelligence (AI) can be used to automate cancer detection. To better classify and for quality improvement of histopathology images, visualization techniques GradCam and SmoothGard are applied. This objective can be achieved by evaluating histopathological images of five types of colon and lung tissues using MobileNetV2 and InceptionResnetV2 models. These proposed models have accurately identified cancer tissues to a maximum of 99.95%. These models will assist medical professionals in the advancement of an automated and authentic system for detecting different types of colon and lung cancers.