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The study introduces ScalpViT, a novel hybrid deep learning model combining a Vision Transformer (ViT) and a Convolutional Neural Network (CNN) to accurately diagnose visually similar scalp diseases, such as Psoriasis and Alopecia Areata. This model addresses the challenge of overlapping visual characteristics in these conditions, which complicates diagnosis. ScalpViT processes scalp images by dividing them into patches and applying self-attention to capture spatial patterns, while the CNN extracts local texture details. The model achieved a 94.3% accuracy on a dataset of approximately 7,000 images, outperforming existing models like ResNet-50 and EfficientNet-B3. ScalpViT also provides visual explainability through GradCAM and Attention Rollout, enhancing its clinical applicability, especially in resource-limited settings.

The study introduces ScalpViT, a novel hybrid deep learning model designed to accurately diagnose visually similar scalp diseases, such as Psoriasis, Seborrheic Dermatitis, Tinea Capitis, Alopecia Areata, Folliculitis, and Eczema. These conditions often share overlapping visual features, making diagnosis challenging. ScalpViT combines a Vision Transformer (ViT) and a Convolutional Neural Network (CNN) with a cross-attention fusion module to capture both global spatial patterns and local texture details. Trained on a dataset of approximately 7,000 images, ScalpViT achieved a 94.3% accuracy, outperforming other models like ResNet-50 and EfficientNet-B3. The model also provides visual explainability through GradCAM and Attention Rollout, enhancing its clinical applicability, especially in resource-limited settings.

The study introduces ScalpViT, a novel hybrid deep learning model designed to accurately diagnose visually similar scalp diseases, such as Psoriasis, Seborrheic Dermatitis, Tinea Capitis, Alopecia Areata, Folliculitis, and Eczema. These conditions often share overlapping visual features, making diagnosis challenging. ScalpViT combines a Vision Transformer (ViT) and a Convolutional Neural Network (CNN) with a cross-attention fusion module to capture both global spatial patterns and local texture details. Trained on a dataset of approximately 7,000 images, ScalpViT achieved a 94.3% accuracy, outperforming other models like ResNet-50 and EfficientNet-B3. The model also provides visual explainability through GradCAM and Attention Rollout, enhancing its clinical applicability, especially in resource-limited settings.

A single case study explored the effectiveness of a comprehensive Ayurvedic treatment for Indralupta, akin to alopecia areata. The treatment involved Shodhana and Shamana therapies, including Dīpana–Pachana, Pracchana Karma, and Takradhara, alongside internal medications and topical applications. The patient showed significant improvement, with reduced patch size, increased hair density, and restored follicular activity, without adverse effects. The study concludes that this integrative Ayurvedic approach may be a safe and effective alternative for managing alopecia areata.

This study assessed hair loss prevalence and its contributing factors among 302 students and faculty at a private pharmacy college in Telangana, India. The findings revealed that 40% of participants experienced mild hair loss, while 19% had significant hair loss. The majority of participants were female (72%) and aged 18–20 years (62%). Hair loss was significantly linked to academic stress, family history, and lifestyle factors, with moderate stress levels observed in most participants. The study highlights the need for mental health strategies to reduce stress and improve well-being, potentially addressing hair loss issues.

This study investigated hair loss (HL) among students and faculty at a private pharmacy college in Telangana, India, involving 302 participants. The research found that 40% of participants experienced mild hair loss, while 19% reported significant hair loss. The study identified academic stress, family history, and lifestyle factors as significant contributors to hair loss, with moderate stress levels prevalent among participants. The findings highlight the need for mental health strategies to reduce stress and improve well-being, potentially addressing hair loss issues.