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TumorDiagX is a cutting‐edge framework that combines deep learning and computer vision to accurately identify and classify cancers. Our collection of colonoscopies 1518 images is meticulously pre‐processed, including greyscale conversion and local binary pattern (LBP) extraction, before being securely stored on the Google Cloud platform. In the second phase, we fully assess three different convolutional neural networks (CNNs): residual network with 50 layers (ResNet‐50), DenseNet‐201 and visual geometry group with 16 layers (VGG‐16). Stage three introduces four integrated CNNs (ResNet‐50+DenseNet‐201 (RD‐22), DenseNet‐201+VGG‐16 (DV‐22), ResNet‐50+VGG‐16 (RV‐22), and ResNet‐50+DenseNet‐201=VGG‐16 (RDV‐22)) to improve cancer detection by combining the capabilities of several networks. Comprehensive analysis and training on the datasets provide significant insights into CNN's performance. The fourth step involves an extensive comparison, integrating and comparing all three data sets using individual and integrated CNNs to determine the best effective models for cancer diagnosis. In this final step, image segmentation leverages an encoder–decoder network, namely a Universal Network (U‐Net) CNN, to aid in the visual detection of malignant cancer lesions. The results highlight the effectiveness of TumorDiagX, with the feature fusion CNN using DenseNet‐201 attaining training and testing accuracies of 97.27% and 97.35%. Notably, CNN (feature fusion) in combination with RDV‐22 performs better, with training and testing accuracy of 98.47% and 97.93%, respectively, and a dice coefficient of 0.92. The information is privately maintained in the cloud and acts as an essential asset for healthcare practitioners, allowing for specific cancer prediction and prompt detection. Our method, with its meticulous performance metrics and multifaceted approach, has the potential to advance early cancer identification and treatment. [ABSTRACT FROM AUTHOR]

"PolynetDWTCADx" is a sophisticated hybrid model that was developed to identify and distinguish colorectal cancer. In this study, the CKHK-22 dataset, comprising 24 classes, served as the introduction. The proposed method, which combines CNNs, DWTs, and SVMs, enhances the accuracy of feature extraction and classification. The study employs DWT to optimize and enhance two integrated CNN models before classifying them with SVM following a systematic procedure. PolynetDWTCADx was the most effective model that we evaluated. It was capable of attaining a moderate level of recall, as well as an area under the curve (AUC) and accuracy during testing. The testing accuracy was 92.3%, and the training accuracy was 95.0%. This demonstrates that the model is capable of distinguishing between noncancerous and cancerous lesions in the colon. We can also employ the semantic segmentation algorithms of the U-Net architecture to accurately identify and segment cancerous colorectal regions. We assessed the model's exceptional success in segmenting and providing precise delineation of malignant tissues using its maximal IoU value of 0.93, based on intersection over union (IoU) scores. When these techniques are added to PolynetDWTCADx, they give doctors detailed visual information that is needed for diagnosis and planning treatment. These techniques are also very good at finding and separating colorectal cancer. PolynetDWTCADx has the potential to enhance the recognition and management of colorectal cancer, as this study underscores., Competing Interests: Declarations. Competing interests: The authors declare no competing interests. Ethics approval: This work uses the human colonoscopy scan dataset, which is public and not collected in real time. Since the data employed in this research were obtained from public domain social media platforms, there was no need for ethics approval for this study., (© 2025. The Author(s).)