Skin lesions are localized regions of the skin exhibiting atypical growth or appearance and can be either benign or malignant. Detecting and classifying these lesions accurately is crucial for early diagnosis and effective treatment of skin cancer. However, existing approaches for classifying cancerous and non-cancerous skin lesions from dermoscopic images are limited in capturing the appropriate features and also susceptible to overfitting in imbalanced datasets. This work aims to improve skin cancer classification by enhancing feature representation in dermoscopic images. A hybrid classification system is proposed, combining two tracks: the Swin Transformer and the Dense Group Shuffle Non-Local Attention (DGSNLA) Network, which integrates DenseNet169, Group Shuffle Depth-wise blocks (GSDW), and an enhanced non-local attention block (ENLA). This deep feature fusion approach effectively aggregates global and local features, resulting in more accurate feature representation and improved classification performance. To the best of our knowledge, this is the first study to combine a transformer with a custom CNN for skin cancer classification. The proposed network achieves an accuracy of 94.21%, an F1-score of 96.64%. a precision of 96.62%, and a recall of 96.25% on the HAM10000 dataset.

Autism Spectrum Disorder (ASD) significantly impacts social communication, interaction, and behavior. Early diagnosis and timely intervention can greatly improve outcomes by enabling tailored therapeutic strategies. Electroencephalography (EEG) has emerged as a non-invasive tool to capture brain activity and facilitate the early detection of ASD using machine learning techniques. However, attaining high accuracy with minimal EEG channels remains a challenge. This study analyzed EEG data from 10 children with ASD and 10 Typically Developed (TD) children using three electrode combinations: C3-C4, C3-Cz, and C4-Cz. EEG signals were spatially filtered using a wavelet-based regularized filter bank common spatial pattern. Key features, including peak-to-peak amplitude, were extracted, and correlation-based feature selection identified the most informative features. Classification with Support Vector Machine (SVM) identified the C4-Cz pair as the most effective, achieving the highest accuracy. Further analysis applied Neural Networks (NN), Quantum Support Vector Machines (QSVM), and Quantum Neural Networks (QNN) to classify data from the C4-Cz pair. QSVM with amplitude embedding feature map outperformed others, achieving an accuracy of 94.7%. Performance was further improved by incorporating an enhanced feature set comprising peak frequency, Stockwell transform coefficients, and peak-to-peak amplitude. The proposed system, leveraging these refined features and QSVM, achieved an exceptional accuracy of 98.9%. To the best of our knowledge, this is the first study utilizing an enhanced feature set derived from reduced brain lobes and quantum machine learning for ASD classification, offering a novel and highly accurate diagnostic approach.

Diastasis recti abdominis (DRA) is more prevalent in women during pregnancy and postpartum. However, there is a lack of awareness regarding this condition among women. The prevalence of DRA is high in late pregnancy and reduces during postpartum. The purpose of this study is to provide an overview of the treatment strategies for DRA and to discuss the significance of the technology towards better diagnosis and treatment. This work investigated 77 research articles published in the recognized research databases. The study aims to analyze the diagnostic and treatment procedures and the role of technology within them. The management strategy for DRA can either be conservative or surgical. Exercise therapy has been shown to improve functional impairments. These exercises focus on recruiting the abdominal muscles. Electromyography and Ultrasound imaging have been employed as useful tools in assessing the abdominal muscles effectively. This study has examined the treatment methods for DRA to obtain a better understanding of the existing methods. Further investigation and experimentation into therapeutic exercises is strongly recommended to identify the best set of exercises for a faster resolution. Further studies regarding the role of technology to assess therapeutic exercises would be worthwhile.