Deep Learning for Equitable Dermatological Diagnosis: Enhansing Healthcare Accessibility

Abstract

Our research presents an innovative deep learning approach designed to enhance dermatologic diagnosis by addressing the significant challenges of subtle color variations and complex lesion morphology. Accurate diagnosis in dermatology is critical, as skin diseases can have profound implications on a patient's health and quality of life. Misdiagnosis can lead to inappropriate treatments, exacerbation of conditions, and increased healthcare costs. To tackle these challenges, our method leverages the strengths of Convolutional Neural Networks (CNNs) with a focus on the efficient and lightweight MobileNet version 3 and MobileNet version 2. CNNs are well-established for their effectiveness in image analysis, and MobileNet v3, in particular, offers a streamlined architecture that is both powerful and resourceefficient. In our research, we trained MobileNet v3 & MobileNet v2 on a large dataset of color-skin images sourced from Kaggle. This dataset provided a diverse and comprehensive collection of dermatologic images necessary for extracting discriminative features from various skin lesions, a crucial step for distinguishing between different types of skin conditions. MobileNet was employed to handle the intricate task of capturing fine-grained details and subtle color variations within the images. Its architecture allows for efficient processing, making it feasible to deploy in real-world clinical settings where computational resources may be limited. This capability allowed for precise lesion segmentation and classification, significantly enhancing diagnostic accuracy. Our model offers several advantages, enabling the collection of both local and global information essential for accurately diagnosing complex skin conditions. Additionally, our approach outperforms traditional singlearchitecture models in image classification tasks, leveraging the specific strengths of MobileNet v3 & MobileNet v2 to achieve superior performance. In conclusion, our research provides a promising solution to the challenges of dermatologic diagnosis, demonstrating superior performance compared to conventional methods and offering more accurate and reliable diagnoses for various skin condition.