  The incidence rate for skin cancer has been steadily increasing throughout
the world, leading to it being a serious issue. Diagnosis at an early stage has
the potential to drastically reduce the harm caused by the disease, however,
the traditional biopsy is a labor-intensive and invasive procedure. In
addition, numerous rural communities do not have easy access to hospitals and
do not prefer visiting one for what they feel might be a minor issue. Using
machine learning and deep learning for skin cancer classification can increase
accessibility and reduce the discomforting procedures involved in the
traditional lesion detection process. These models can be wrapped in web or
mobile apps and serve a greater population. In this paper, two such models are
tested on the benchmark HAM10000 dataset of common skin lesions. They are
Random Forest with Stratified K-Fold Validation, and MobileNetV2 (throughout
the rest of the paper referred to as MobileNet). The MobileNet model was
trained separately using both TensorFlow and PyTorch frameworks. A side-by-side
comparison of both deep learning and machine learning models and a comparison
of the same deep learning model on different frameworks for skin lesion
diagnosis in a resource-constrained mobile environment has not been conducted
before. The results indicate that each of these models fares better at
different classification tasks. For greater overall recall, accuracy, and
detection of malignant melanoma, the TensorFlow MobileNet was the better
choice. However, for detecting noncancerous skin lesions, the PyTorch MobileNet
proved to be better. Random Forest was the better algorithm when it came to
having a low computational cost with moderate correctness.
