An advanced diagnostic ColoRectalCADx utilises CNN and unsupervised visual explanations to discover malignancies

Colorectal cancer (CRC) is one of the most lethal kinds of cancer, so early detection is critical. Three datasets, namely CNN transfer learning with discrete wavelet transform (DWT), discrete cosine transform (DCT), and support vector machines (SVMs), were used to find CRC. In these instances, a quick and precise visual diagnosis of polyps is needed in the current scenario. The proposed process involves four distinct phases. First and foremost, convolutional neural networks (CNNs) are developed to test the efficacy of the model. Further, a transfer learning approach was incorporated using SVM and LSTM. Using the K-means technique, a visual explanation is finally presented. This system works with the balanced Hyper Kvasir and mixed datasets, which are made up of CVC Clinic DB, Kvasir2, and Hyper Kvasir. The system is called "ColoRectalCADx". The convolutional neural network (CNN) models are ResNet-50V2, DenseNet-201, VGG-16, and RDV-22. The system achieved the highest accuracy with CNN DesnseNet-201 in Hyper Kvasir (98.92% training, 98.91% testing, 93.62% SVM training, and 95.87% SVM tests). CNN DenseNet-201 also achieved the highest accuracy with the mixed dataset (98.91% training, 96.13% testing, 95.41% SVM training, and 94.86% SVM testing). The process involved three phases, namely individual CNN, combination of CNN with SVM, and combination of CNN, LSTM, and SVM. After three phases of the system, across both datasets, the CNN + SVM + LSTM combination was proven to be the most effective. Finally, the unsupervised K-means learning algorithm extracts the location of any cancerous polyps and upon classification using SVM classifier resulted with an accuracy of 80%. The K-means algorithm, which uses segmented images as input, accurately predicts the sites of tumours in colorectal cancer patients.