Skin cancer is a significant global health concern, and accurate and timely diagnosis is crucial for successful treatment. However, manual diagnosis can be challenging due to the subtle visual differences between benign and malignant lesions. This study introduces Skin-DeepNet, a novel deep learning-based framework designed for the automated early diagnosis and classification of skin cancer lesions from dermoscopy images. Skin-DeepNet incorporates a two-step pre-processing stage to enhance image contrast, followed by robust skin lesion segmentation using Mask R-CNN and GrabCut algorithm to achieve near-perfect segmentation accuracy (IOU up to 99.93%). Then a dual-feature extraction strategy is performed using a combination of a pre-trained high-resolution network (HRNet) model and attention block, which serve as feature descriptors. A deep belief networks (DBN) model is then trained on their outputs to capture high-level discriminative features. Finally, robust decision fusion strategies are employed to integrate the predictions of the proposed models using boosting and stacking to enhance overall Skin-DeepNet’s accuracy. The Skin-DeepNet’s performance has been validated on two challenging datasets: ISIC 2019 and HAM1000. The Skin-DeepNet system has outperformed the existing state-of-the-art systems by achieving an accuracy rate of 99.65% Precision of 99.51%, AUC of 99.94% on the ISIC 2019 dataset. Similarly, on the HAM1000 dataset, the Skin-DeepNet system demonstrated an accuracy rate, precision, and AUC of 100%, 99.92%, and 99.97%, respectively. These findings indicate that the developed Skin-DeepNet system can exhibit outstanding proficiency in accurately classifying skin lesions while aiding physicians in early diagnosis and treatment tasks in clinical settings.

Accurate segmentation of white blood cells (WBCs) in microscopic blood smear images is critical for diagnosing haematological disorders such as leukemia. However, manual analysis by pathologists remains labor-intensive and prone to variability, necessitating automated solutions. This study proposes a color-based WBCs segmentation framework leveraging the HSV color space and Differential Evolution (DE) optimization approach to address challenges posed by heterogeneous staining, overlapping cells, and imaging artefacts. The proposed WBCs segmentation framework operates in three stages: pre-processing via contrast enhancement and HSV transformation, region-based segmentation using DE-optimized dual-thresholding, and post-processing with morphological operations. The DE approach can learn to adaptively determine best-fit HSV thresholds for the separation of WBCs without heavy dependence on annotated datasets or deep learning architectures. Experimental analysis on the BCCD and ALL-IDB2 datasets demonstrates superior performance, with IoU scores of 99.56% and 98.78%, Dice coefficients of 97.23% and 99.53%, and F1-scores of 99.44% and 99.18%, respectively. By combining stochastic optimization with color space analysis, the presented framework yields a computationally efficient, interpretable alternative to complex deep learning models, enabling robust WBCs segmentation in resource-constrained clinical environments. The study refers to the potential of the proposed framework for incorporation into computer-automated diagnostic systems, which would minimize the dependence on manual analysis and enhance diagnostic throughput and consistency.

Human activity Recognition (HAR) has emerged as an important research area due to its potential applications in health, sport, and recreation. The widespread availability of smartphone sensors has facilitated data collection for HAR systems. Although machine learning and deep learning models have proven to be effective in detecting human activity from sensor data, their performance may be limited, this study proposes MotionFusion which is an ensemble learning model to increase HAR accuracy utilizing accelerometer and gyroscope data from a smartphone. By combining Histogram-Based Gradient Boosting, Random Forest, and Extra Trees models with a Support Vector Machine classifier and using feature selection techniques, we aim to classify six common functions accurately: sit, lie down, walk, stand, walk up and/down. A comprehensive evaluation was conducted using a well-established benchmark dataset, the UCI-HAR dataset that was collected from two sensors (accelerometer and gyroscope). The experimental results show that the MotionFusion model achieves more than 99% classification accuracy when using data from both sensors. Moreover, it obtained high accuracy rates of 99% and 98% when using accelerometer and gyroscope sensor data independently.