Colorectal cancer typically affects the gastrointestinal tract within the human body. Colonoscopy is one of the most accurate methods of detecting cancer. The current system facilitates the identification of cancer by computer-assisted diagnosis (CADx) systems with a limited number of deep learning methods. It does not imply the depiction of mixed datasets for the functioning of the system. The proposed system, called ColoRectalCADx, is supported by deep learning (DL) models suitable for cancer research. The CADx system comprises five stages: convolutional neural networks (CNN), support vector machine (SVM), long short-term memory (LSTM), visual explanation such as gradient-weighted class activation mapping (Grad-CAM), and semantic segmentation phases. Here, the key components of the CADx system are equipped with 9 individual and 12 integrated CNNs, implying that the system consists mainly of investigational experiments with a total of 21 CNNs. In the subsequent phase, the CADx has a combination of CNNs of concatenated transfer learning functions associated with the machine SVM classification. Additional classification is applied to ensure effective transfer of results from CNN to LSTM. The system is mainly made up of a combination of CVC Clinic DB, Kvasir2, and Hyper Kvasir input as a mixed dataset. After CNN and LSTM, in advanced stage, malignancies are detected by using a better polyp recognition technique with Grad-CAM and semantic segmentation using U-Net. CADx results have been stored on Google Cloud for record retention. In these experiments, among all the CNNs, the individual CNN DenseNet-201 (87.1% training and 84.7% testing accuracies) and the integrated CNN ADaDR-22 (84.61% training and 82.17% testing accuracies) were the most efficient for cancer detection with the CNN+LSTM model. ColoRectalCADx accurately identifies cancer through individual CNN DesnseNet-201 and integrated CNN ADaDR-22. In Grad-CAM’s visual explanations, CNN DenseNet-201 displays precise visualization of polyps, and CNN U-Net provides precise malignant polyps.
<span>The malignancy of the colorectal testing methods has been exposed triumph to decrease the occurrence and death rate; this cancer is the relatively sluggish rising and has an extremely peculiar to develop the premalignant lesions. Now, many patients are not going to colorectal cancer screening, and people who do, are able to diagnose existing tests and screening methods. The most important concept of this motivation for this research idea is to evaluate the recognized data from the immediately available colorectal cancer screening methods. The data provided to laboratory technologists is important in the formulation of appropriate recommendations that will reduce colorectal cancer. With all standard colon cancer tests can be recognized agitatedly, the treatment of colorectal cancer is more efficient. The intelligent computer assisted diagnosis (CAD) is the most powerful technique for recognition of colorectal cancer in recent advances. It is a lot to reduce the level of interference nature has contributed considerably to the advancement of the quality of cancer treatment. To enhance diagnostic accuracy intelligent CAD has a research always active, ongoing with the deep learning and machine learning approaches with the associated convolutional neural network (CNN) scheme.</span>
The catastrophic impacts of the lethal illness known as intestinal cancer are felt by those who suffer from it. Colonoscopy is the most effective cancer screening procedure currently available. While several deep learning methods are already used by existing systems, little research has been conducted on feature extraction and testing in the context of cancer diagnosis. The suggested system is a more comprehensive implementation of CADx technology that relies on intelligent detection. Data from the CKHK-22 dataset was preprocessed and kept in the cloud before being utilized for the system's training and testing purposes. To create local binary pattern (LBP) features, we first grayscale the colour features included in the colonoscopy dataset images. Several convolutional neural networks (CNNs), including VGG-16, DenseNet-201, and ResNet-50, are trained on the CKHK-22 dataset after it has been transformed to three features. In a vital step, the CKHK-22's feature acquisitions are fused to produce new features that are then employed in the DV-22, RD-22, and RDV-22 fusion CNN models, respectively. The CADx system, transfer learning with LSTM, and DenseNet-201 all attained testing accuracy rates of 91.92%, 86.76%, and 68.56%, respectively, over the original, grayscale, and LBP image feature datasets. By initially collecting the combined features of the original, grey, and LBP features through transfer learning with LSTM, the RDV-22 model obtained the best testing accuracy (90.81%) while applying fusion CNN for feature acquisition. The CKHK-22 dataset was mined for its three most distinguishing features by this CADx system, which yielded sufficient accuracy with DenseNet-201 and merged features and optimal accuracy with RDV-22 fusion CNN in diagnosing cancer. Using feature extraction, cancer diagnoses may be predicted with high precision.
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