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.

The complexity and variability of medical imaging continue to impair the feasibility of early detection of colorectal cancer, despite its critical role in improving patient outcomes. This research presents a new multistage ensemble method that combines the strengths of three advanced deep learning models: vision transformers, RDV-22 (a mix of ResNet-50, DenseNet-201, and VGG-16), and Bidirectional Long Short-Term Memory (BiLSTM) networks. It improves the accuracy and robustness of Colorectal Cancer (CRC) detection. For suitable evaluation of our methodology, we adopted two benchmark datasets: CVC Clinic DB for binary classification and Kvasir dataset for multiclass classifications. To decrease the sizes of data significantly along with the generalizability of the model, we have used various data augmentation techniques, including magnification, inversion, and rotation. The best performing model was RDV-22 + BiLSTM + Vision Transformers, which recorded a 95.0% test accuracy and an area of 0.96 under the curve with the CVC Clinic DB dataset. The model showed a high performance in wide classes of samples such as esophagitis, polyps, and ulcerative colitis, with an accuracy rate of 92.5% and Area Under Curve (AUC) values 1.00 on the Kvasir dataset. We applied Local Interpretable Model-Agnostic Explanations (LIME) visualizations to both datasets, which proved to be an insightful identification of regions that the model relied on during the polyp detection process. Based on the LIME explanation, this analysis found that the visualizations generated by the Kvasir dataset were more informative and even clearer when complex cases, including ulcerative colitis and polyps are concerned. This indicates the model's ability to handle challenging medical images effectively. Because of the complexity and diversity of the datasets, these results constitute a significant improvement on the current methodology. There is expectation that the proposed ensemble method could significantly improve the dependability and precision of diagnosing colorectal cancer. More accurate diagnoses and faster diagnoses in clinical practice might improve patient outcomes and reduce death rates if this is successful. While the ensemble model presents highly improved performance, this computationally and dataset-specific performance requirement suggests further optimization and validation for larger clinical applicability.

In this paper a new approach is proposed to classify leafs in efficient and effective manner. In this approach, the features are extracted from edges of the leaf images and it is used two kinds of features which are edge based and shape based features for leaf classification. When the present method is tested on Flavia dataset, it gives the average classification accuracy rate of 94.37%. The Flavia dataset contains 32 kinds of plant leaves. The experimental result shows that the method gives better performance results compared with existing methods.

Early detection of colorectal carcinoma (CRC), one of the most prevalent forms of cancer worldwide, significantly enhances the prognosis of patients. This research presents a new method for improving CRC detection using a deep learning ensemble with the Computer Aided Diagnosis (CADx). The method involves combining pre-trained convolutional neural network (CNN) models, such as ADaRDEV2I-22, DaRD-22, and ADaDR-22, using Vision Transformers (ViT) and XGBoost. The study addresses the challenges associated with imbalanced datasets and the necessity of sophisticated feature extraction in medical image analysis. Initially, the CKHK-22 dataset comprised 24 classes. However, we refined it to 14 classes, which led to an improvement in data balance and quality. This improvement enabled more precise feature extraction and improved classification results. We created two ensemble models: the first model used Vision Transformers to capture long-range spatial relationships in the images, while the second model combined CNNs with XGBoost to facilitate structured data classification. We implemented DCGAN-based augmentation to enhance the dataset’s diversity. The tests showed big improvements in performance, with the ADaDR-22 + Vision Transformer group getting the best results, with a testing accuracy of 93.4% and an AUC of 98.8%. In contrast, the ADaDR-22 + XGBoost model had an AUC of 97.8% and an accuracy of 92.2%. These findings highlight the efficacy of the proposed ensemble models in detecting CRC and highlight the importance of using well-balanced, high-quality datasets. The proposed method significantly enhances the clinical diagnostic accuracy and the capabilities of medical image analysis or early CRC detection.

Colorectal cancer is associated with a high mortality rate and significant patient risk. Images obtained during a colonoscopy are used to make a diagnosis, highlighting the importance of timely diagnosis and treatment. Using techniques of deep learning could enhance the diagnostic accuracy of existing systems. Using the most advanced deep learning techniques, a brand-new EnsemDeepCADx system for accurate colorectal cancer diagnosis has been developed. The optimal accuracy is achieved by combining Convolutional Neural Networks (CNNs) with transfer learning via bidirectional long short-term memory (BILSTM) and support vector machines (SVM). Four pre-trained CNN models comprise the ADaDR-22, ADaR-22, and DaRD-22 ensemble CNNs: AlexNet, DarkNet-19, DenseNet-201, and ResNet-50. In each of its stages, the CADx system is thoroughly evaluated. From the CKHK-22 mixed dataset, colour, greyscale, and local binary pattern (LBP) image datasets and features are utilised. In the second stage, the returned features are compared to a new feature fusion dataset using three distinct CNN ensembles. Next, they incorporate ensemble CNNs with SVM-based transfer learning by comparing raw features to feature fusion datasets. In the final stage of transfer learning, BILSTM and SVM are combined with a CNN ensemble. The testing accuracy for the ensemble fusion CNN DarD-22 using BILSTM and SVM on the original, grey, LBP, and feature fusion datasets was optimal (95.96%, 88.79%, 73.54%, and 97.89%). Comparing the outputs of all four feature datasets with those of the three ensemble CNNs at each stage enables the EnsemDeepCADx system to attain its highest level of accuracy.