Improving Skin Lesion Diagnosis: Hybrid Blur Detection for Accurate Dermatological Image Analysis

Accurate diagnosis of skin lesions is crucial for early detection and effective treatment of dermatological conditions. However, blurry artifacts present in dermatological images can significantly hinder diagnostic accuracy. Existing research primarily focuses on either shape analysis or deep learning techniques individually, with limited consideration of hybrid approaches that can leverage the complementary strengths of both methodologies. To address this research gap, we propose a novel hybrid blur detection method for enhancing skin lesion diagnosis. Our approach integrates shape analysis techniques with deep learning methodologies to improve the accuracy of dermatological image analysis. Shape analysis algorithms capture intricate shape features of skin lesions, which are then utilized by a deep learning model trained on a diverse dataset of dermatological images. Experimental evaluations demonstrate the effectiveness of our hybrid approach in accurately identifying and localizing blur regions within skin lesion images. By mitigating the impact of blurry artifacts, our method enhances image quality and facilitates accurate analysis, enabling early detection and intervention for improved patient outcomes. This research contributes to the advancement of skin lesion diagnosis by providing a robust tool for clinicians and dermatologists. The proposed hybrid blur detection method has the potential to significantly improve the precision and reliability of dermatological image analysis, leading to more accurate diagnoses and timely treatment decisions.