Enhanced Skin Disease Diagnosis through Convolutional Neural Networks and Data Augmentation Techniques

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ID: 312837
2024
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Abstract
Skin diseases are among the most common and widespread diseases affecting people around the world. Global warming and climate change are the two main factors leading to skin cancer. Skin diseases can be life-threatening if they are not detected and treated early. Clinical diagnosis of dermatological diseases is a relatively rare and expensive procedure, inaccessible to the general population. Advanced machine learning and image processing technologies enable feature extraction and disease identification. Deep learning is one of the emerging fields of machine learning that uses advanced intelligent techniques to classify images based on various features. In this research work multiple several deep learning models based on convolutional neural network (CNN) architecture such as sequential CNN model, DenseNet-121 and ResNet-50 are used for feature extraction. The HAM10000 dataset is used to evaluate the accuracy of the proposed model. There are 10015 images in the HAM10000 collection, which have been divided into seven different classes of skin diseases. The data set is divided into a training set and a test set, with ratios of 20% and 80%, respectively. Diagnosing skin diseases is a three-step process: first pre-processing the images of the dataset, then extracting features using CNN model in the second step and ends with classification of the skin disease category using various classifiers based on the features extracted in the third stage. Techniques for image augmentation were applied to lower the imbalance between different categories of skin diseases. The sequential CNN-based model with seven convolution layers achieves an accuracy of 98% and 99% for each of the seven categories of skin diseases in the Area Under the Curve (AUC). DenseNet-121 and ResNet-50 provide accuracy values of 89% and 84%, respectively. Various performance matrices are used to compare and evaluate the effectiveness of various CNN models on the provided dataset.
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Authors Abdul Rafay
Journal Journal of Computing & Biomedical Informatics
Year 2024
DOI
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URL
https://jcbi.org/index.php/Main/article/view/480
Keywords Keywords not found

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