Breast cancer detection heavily relies on medical imaging, particularly ultrasound, for early diagnosis and effective treatment. This research addresses the challenges associated with computer-aided diagnosis (CAD) of breast cancer from ultrasound images. The primary challenge is accurately distinguishing between malignant and benign tumors, complicated by factors such as speckle noise, variable image quality, and the need for precise segmentation and classification. The main objective of the research paper is to develop an advanced methodology for breast ultrasound image classification, focusing on speckle noise reduction, precise segmentation, feature extraction, and machine learning-based classification. A unique approach is introduced that combines Enhanced Speckle Reduced Anisotropic Diffusion (SRAD) filters for speckle noise reduction, U-NET-based segmentation, Genetic Algorithm (GA)-based feature selection, and Random Forest and Bagging Tree classifiers, resulting in a novel and efficient model. To test and validate the hybrid model, rigorous experimentations were performed and results state that the proposed hybrid model achieved accuracy rate of 99.9%, outperforming other existing techniques, and also significantly reducing computational time. This enhanced accuracy, along with improved sensitivity and specificity, makes the proposed hybrid model a valuable addition to CAD systems in breast cancer diagnosis, ultimately enhancing diagnostic accuracy in clinical applications.

Brain signal analysis from electroencephalogram (EEG) recordings is the gold standard for diagnosing various neural disorders especially epileptic seizure. Seizure signals are highly chaotic compared to normal brain signals and thus can be identified from EEG recordings. In the current seizure detection and classification landscape, most models primarily focus on binary classification—distinguishing between seizure and non-seizure states. While effective for basic detection, these models fail to address the nuanced stages of seizures and the intervals between them. Accurate identification of per-seizure or interictal stages and the timing between seizures is crucial for an effective seizure alert system. This granularity is essential for improving patient-specific interventions and developing proactive seizure management strategies. This study addresses this gap by proposing a novel AI-based approach for seizure stage classification using a Deep Convolutional Neural Network (DCNN). The developed model goes beyond traditional binary classification by categorizing EEG recordings into three distinct classes, thus providing a more detailed analysis of seizure stages. To enhance the model’s performance, we have optimized the DCNN using two advanced techniques: the Stochastic Gradient Algorithm (SGA) and the evolutionary Genetic Algorithm (GA). These optimization strategies are designed to fine-tune the model’s accuracy and robustness. Moreover, k-fold cross-validation ensures the model’s reliability and generalizability across different data sets. Trained and validated on the Bonn EEG data sets, the proposed optimized DCNN model achieved a test accuracy of 93.2%, demonstrating its ability to accurately classify EEG signals. In summary, the key advancement of the present research lies in addressing the limitations of existing models by providing a more detailed seizure classification system, thus potentially enhancing the effectiveness of real-time seizure prediction and management systems in clinical settings. With its inherent classification performance, the proposed approach represents a significant step forward in improving patient outcomes through advanced AI techniques.