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El Gannour, Oussama
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Electrical & Electronics Engineering

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Improving skin diseases prediction through data balancing via classes weighting and transfer learning El Gannour, Oussama; Hamida, Soufiane; Lamalem, Yasser; Mahjoubi, Mohamed Amine; Cherradi, Bouchaib; Raihani, Abdelhadi
Bulletin of Electrical Engineering and Informatics Vol 13, No 1: February 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i1.5999

Abstract

Skin disease prediction using artificial intelligence has shown great potential in improving early diagnosis and treatment outcomes. However, the presence of class imbalance within skin disease datasets poses a significant challenge for accurate prediction, particularly for rare diseases. This study proposes a novel approach to address class imbalance through data balancing using classes weighting, coupled with transfer learning techniques, to enhance the performance of skin disease prediction models. Two experiments were conducted using a tuned EfficientNetV2L based classifier. In the first experiment, a default dataset structure was utilized for training and testing. The second experiment involved employing classes weighting approach to balance the dataset. The effectiveness of the proposed approach is evaluated using the ISIC 2018 dataset, which comprises a diverse collection of skin lesion images. By assigning appropriate weights to different classes based on their prevalence, the proposed method aims to balance the representation of rare disease classes. To evaluate the performance of the proposed methodology, several performance evaluation metrics, including accuracy, precision, and recall, were employed. These findings revealed that the balanced dataset achieved enhanced generalization, mitigating the biases associated with class imbalance. As a result, the efficacy of artificial intelligence models is enhanced.
Toward enhanced skin disease classification using a hybrid RF-DNN system leveraging data balancing and augmentation techniques Hamida, Soufiane; Lamrani, Driss; El Gannour, Oussama; Saleh, Shawki; Cherradi, Bouchaib
Bulletin of Electrical Engineering and Informatics Vol 13, No 1: February 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i1.6313

Abstract

Significant health concerns are associated with skin diseases, and accurate and timely diagnosis is essential for effective treatment and patient management. To improve the classification of cutaneous diseases, we propose a novel hybrid system that incorporates the strengths of random forest (RF) and deep neural network (DNN) algorithms. The system employs data augmentation and balancing techniques to enhance model performance and generalizability. The HAM10000 dataset of diverse dermatoscopic images is used for training and evaluation in this study. In the hybrid system proposed, the RF model provides an initial diagnosis based on patient-reported symptoms, while the DNN analyzes images of skin lesions, resulting in more precise and efficient diagnoses. Using hyper-parameter optimization, we fine-tune the system for optimal performance. The evaluation demonstrates the accuracy of the hybrid model, which achieves a classification accuracy of 96.8% overall. According to our findings, the hybrid system demonstrates exceptional efficacy in six of seven skin disease classes. Variations in sensitivity and reliance on data quality and quantity are however cited as limitations. Nevertheless, this hybrid system has the potential to revolutionize skin disease diagnosis and treatment.
Co-Authors Cherradi, Bouchaib Hamida, Soufiane Lamalem, Yasser Lamrani, Driss Mahjoubi, Mohamed Amine Raihani, Abdelhadi Saleh, Shawki