Abstract:
"Skin cancer poses a significant health risk, demanding precise diagnosis to ensure prompt
intervention. Human-based classification of skin cancer subtypes is prone to inconsistency,
potentially causing delays and misdiagnoses. With the rising cases of skin cancer, an accurate
and efficient approach for early detection is imperative. Current automated systems lack the
required precision and accessibility. Hence, an innovative deep learning solution is essential to
address these challenges and offer dependable subtype classification.
Our endeavor, SkinSafe, introduces a web-based platform that harnesses the capabilities of
deep learning to automate the classification of skin cancer subtypes. Leveraging an extensive
dataset of diverse skin images, we train a deep neural network model. This model proficiently
distinguishes between various lesions, such as melanoma, basal cell carcinoma, and squamous
cell carcinoma. Through a user-friendly web interface, individuals can seamlessly upload their
skin images for processing and classification. SkinSafe incorporates advanced image
preprocessing, data augmentation, and deep learning architectures, fortifying the accuracy and
reliability of the classification process.
Comprehensive evaluations robustly validate SkinSafe's effectiveness, with the model
achieving an impressive 74% F1 score. The system notably outperforms prevailing methods in
both precision and accuracy. Its true strength is in supporting medical practitioners, amplifying
diagnostic speed and precision, thereby enhancing treatment efficacy. User feedback and
acceptance studies underscore the system's ease of use and practicality, solidifying its potential
for widespread adoption within clinical contexts.
"
