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.
