Unsupervised end-to-end Brain Tumor Magnetic Resonance Image Registration using RBCNN: Rigid Transformation, B-Spline Transformation and Convolutional Neural Network

Brain Tumor Detection and Classification Using an Optimized Convolutional Neural Network

Brain tumors are a leading cause of death globally, with numerous types varying in malignancy, and only 12% of adults diagnosed with brain cancer survive beyond five years. This research introduces a hyperparametric convolutional neural network (CNN) model to identify brain tumors, with significant practical implications. By fine-tuning the hyperparameters of the CNN model, we optimize feature extraction and systematically reduce model complexity, thereby enhancing the accuracy of brain tumor diagnosis. The critical hyperparameters include batch size, layer counts, learning rate, activation functions, pooling strategies, padding, and filter size. The hyperparameter-tuned CNN model was trained on three different brain MRI datasets available at Kaggle, producing outstanding performance scores, with an average value of 97% for accuracy, precision, recall, and F1-score. Our optimized model is effective, as demonstrated by our methodical comparisons with state-of-the-art approaches. Our hyperparameter modifications enhanced the model performance and strengthened its capacity for generalization, giving medical practitioners a more accurate and effective tool for making crucial judgments regarding brain tumor diagnosis. Our model is a significant step in the right direction toward trustworthy and accurate medical diagnosis, with practical implications for improving patient outcomes.

A Multi-Stage Faster RCNN-Based iSPLInception for Skin Disease Classification Using Novel Optimization

Nowadays, skin cancer is considered a serious disorder in which early identification and treatment of the disease are essential to ensure the stability of the patients. Several existing skin cancer detection methods are introduced by employing deep learning (DL) to perform skin disease classification. Convolutional neural networks (CNNs) can classify melanoma skin cancer images. But, it suffers from an overfitting problem. Therefore, to overcome this problem and to classify both benign and malignant tumors efficiently, the multi-stage faster RCNN-based iSPLInception (MFRCNN-iSPLI) method is proposed. Then, the test dataset is used for evaluating the proposed model performance. The faster RCNN is employed directly to perform image classification. This may heavily raise computation time and network complications. So, the iSPLInception model is applied in the multi-stage classification. In this, the iSPLInception model is formulated using the Inception-ResNet design. For candidate box deletion, the prairie dog optimization algorithm is utilized. We have utilized two skin disease datasets, namely, ISIC 2019 Skin lesion image classification and the HAM10000 dataset for conducting experimental results. The methods' accuracy, precision, recall, and F1 score values are calculated, and the results are compared with the existing methods such as CNN, hybrid DL, Inception v3, and VGG19. With 95.82% accuracy, 96.85% precision, 96.52% recall, and 0.95% F1 score values, the output analysis of each measure verified the prediction and classification effectiveness of the method.

Optimizing rice plant disease detection with crossover boosted artificial hummingbird algorithm based AX-RetinaNet

Rice is the most important cereal food crop in the world, and half of the world's population uses rice as a staple food for its energy source. The yield production qualities and quantities are affected by biotic and abiotic factors namely viruses, soil fertility, bacteria, pests, and temperature. Rice plant disease is the most crucial factor behind communal, economic, and agricultural losses in the agricultural field. Farmers detect and identify diseases through the naked eye, which takes more time and resources, leading to crop loss and unhealthy farming. To overcome these issues, this paper presents a novel rice plant disease detection approach named the crossover boosted artificial hummingbird algorithm based AX-RetinaNet (CAHA-AXRNet) approach. This current research paper mainly concentrates on the effectiveness of rice plant disease detection and classification. The hyperparameters of the AX-RetinaNet model are optimized through the CAHA optimization model. In this paper, three types of disease detection datasets namely rice plant dataset, rice leaf dataset, and rice disease dataset are included to classify rice plants as healthy or unhealthy. The most essential performance metrics are precision, F1-score, accuracy, specificity, and recall, employed to validate the effectiveness of disease detection. The proposed CAHA-AXRNet approach demonstrates its effectiveness compared to other existing rice plant disease detection methods and achieved an accuracy rate of 98.1%.