Breast cancer is the second leading cause of mortality among women. Early and accurate detection plays a crucial role in lowering its mortality rate. Timely detection and classification of breast cancer enable the most effective treatment. Convolutional neural networks (CNNs) have significantly improved the accuracy of tumor detection and classification in medical imaging compared to traditional methods. This study proposes a comprehensive classification technique for identifying breast cancer, utilizing a synthesized CNN, an enhanced optimization algorithm, and transfer learning. The primary goal is to assist radiologists in rapidly identifying anomalies. To overcome inherent limitations, we modified the Ant Colony Optimization (ACO) technique with opposition-based learning (OBL). The Enhanced Ant Colony Optimization (EACO) methodology was then employed to determine the optimal hyperparameter values for the CNN architecture. Our proposed framework combines the Residual Network-101 (ResNet101) CNN architecture with the EACO algorithm, resulting in a new model dubbed EACO–ResNet101. Experimental analysis was conducted on the MIAS and DDSM (CBIS-DDSM) mammographic datasets. Compared to conventional methods, our proposed model achieved an impressive accuracy of 98.63%, sensitivity of 98.76%, and specificity of 98.89% on the CBIS-DDSM dataset. On the MIAS dataset, the proposed model achieved a classification accuracy of 99.15%, a sensitivity of 97.86%, and a specificity of 98.88%. These results demonstrate the superiority of the proposed EACO–ResNet101 over current methodologies.

The pervasive contamination of aquatic ecosystems by microplastics (MPs), defined as plastic particles <5 mm, poses a significant threat to marine life and human health. Current methods for their analysis, primarily involving visual counting under microscopes followed by spectroscopic validation, are labor-intensive, time-consuming, and prone to human error. This study presents a robust, automated machine learning (ML) framework for the detection and quantification of microplastics from digital microscopy images of water samples. We developed a pipeline that utilizes a deep learning object detection model, YOLOv7, to accurately identify and classify MPs based on size and shape (e.g., fibers, fragments, beads). Subsequently, a pixel-wise segmentation model, U-Net, is employed for precise quantification of particle dimensions. We curated a novel dataset of over 5,000 annotated microscope images from water samples collected from various aquatic sources. The YOLOv7 model achieved a mean Average Precision (mAP@0.5) of 96.8% in detecting MPs, while the U-Net model achieved a Dice coefficient of 0.94 for particle segmentation. Our system significantly reduces analysis time from hours per sample to minutes, with a high degree of accuracy and reproducibility. This approach provides a scalable, efficient, and accessible tool for environmental monitoring agencies and researchers, enabling large-scale mapping and monitoring of microplastic pollution.