Abstract:
A significant and important part of our life is our health, and integrating computers into
our daily lives has the potential to improve our health. Healthcare could undergo a radical
change if a machine learning model that can identify a benign or cancerous skin mole from
a picture is developed. This paper performs a comprehensive review with an emphasis on
the developing body of research on the use of convolutional neural networks (CNNs) to
classify skin lesions. The focus is on classifiers that are especially made for skin lesions;
techniques that use CNNs only for segmentation or classification of thermoscopic patterns
are not included. The paper explores the difficulties in comparing various processes and
discusses the issues that need to be resolved in other studies. The implementation uses the
Python programming language and a CNN model in Keras with TensorFlow as the
backend. ISIC databases that are open to the public are used for training and testing in this
research, which is carried out on the Kaggle platform. The collection consists of balanced
photos of skin moles that are either benign or cancerous, arranged into two files. Owing to
the magnitude of the dataset, only 100% of the total training data is designated for testing
when the training and test sets are combined. Seven percent of the dataset is reserved for
validation. Various CNN architectures, such as MobileNetv2, efficientNet, and
RandomForestClassifier, are employed for analysis. Notably, RandomForestClassifier
exhibits the highest accuracy in this scenario. The selected best model undergoes an
ablation study based on hyperparameters, with RandomForestClassifier achieving
exemplary results: Train Accuracy of 99% and Test Accuracy of 98.37%.
