Search

Breast cancer represents a leading cause of mortality among women. Consequently, early detection is crucial for effective screening and reducing the overall death rate. Therefore, the development of computer-aided diagnosis systems is essential for identifying breast cancer in its initial stages. Because of its affordability, ultrasound serves as a vital examination tool for breast cancer diagnosis. Although numerous learning-based methods have recently been introduced to categorize breast cancer using the breast ultrasound imaging (BUSI) dataset, manual processing remains a difficult and highly time-consuming task. This study presents an XAI-based framework utilizing an EfficientNet-integrated ResNet deep network to precisely categorize breast cancer as either benign or malignant. Initially, data augmentation is applied to expand the training sample size. To achieve this, three-pixel flip mathematical equations are implemented: horizontal, vertical, and 90°. Subsequently, two pre-trained deep learning models are utilized, selectively skipping specific layers before fine-tuning. These adjusted models are then trained via a deep transfer learning process to extract features from their deeper layers. The performance of these trained models is evaluated utilizing explainable artificial intelligence (XAI). Following this, a novel feature selection approach known as cuckoo search controlled standard error mean is introduced, which is grounded in the cuckoo search algorithm. This method isolates the optimal features, which are then combined using a novel parallel zero-padding maximum correlated coefficient fusion technique. Finally, the selection algorithm is re-applied to the integrated feature vector prior to classification via machine learning algorithms. Experimental evaluations of the proposed framework, performed on the publicly accessible BUSI dataset, achieved accuracies of 98.4% and 98% across two distinct experiments. Comparisons with contemporary techniques demonstrate that the proposed framework offers enhanced accuracy. Furthermore, it requires less execution time compared to the standalone original deep learning models.

Equatorial plasma bubbles (EPBs) represent areas of reduced electron density that develop within Earth’s ionosphere as a result of Rayleigh–Taylor instability. These formations can trigger signal scintillation, which induces signal degradation and navigation positioning inaccuracies. EPBs can be identified via imagery gathered by All-Sky Imager (ASI) networks. The present research introduces an inexpensive, automated technique for identifying EPBs within ASI datasets, suitable for real-time monitoring as well as categorization tasks. This approach couples Two-Dimensional Principal Component Analysis (2DPCA) and Recursive Feature Elimination (RFE) alongside a Random Forest machine learning classifier to construct an Explainable Artificial Intelligence (XAI) framework. This framework extracts image characteristics to automate EPB detection using minimal dimensionality, yielding a compact and exceptionally rapid-to-train model designed to discern EPBs in recorded ASI frames. A collection of 2458 images, categorized into "Event" and "Empty" classes, was assembled to construct the database. This image repository was partitioned randomly into two distinct portions: an 80% training dataset and a 20% testing dataset. The developed XAI framework achieved a marginally better detection accuracy relative to the conventional 2DPCA approach while maintaining a notably smaller structural footprint. Additionally, the execution of the proposed framework was assessed and contrasted against alternative deep learning baselines (ResNet18, Inception-V3, VGG16, and VGG19) under identical environmental conditions.