A Computer-Aided Diagnosis System Using Deep Learning for Multiclass Skin Lesion Classification

• Published in: Computational intelligence and neuroscience Vol. 2021; no. 1; p. 9619079
• Main Authors: Arshad, Mehak, Khan, Muhammad Attique, Tariq, Usman, Armghan, Ammar, Alenezi, Fayadh, Younus Javed, Muhammad, Aslam, Shabnam Mohamed, Kadry, Seifedine
• Format: Journal Article
• Language: English
• Published: United States Hindawi 2021; John Wiley & Sons, Inc
• Subjects: Accuracy; Algorithms; Augmentation; Automation; Brain cancer; Cancer; Classification; Computers; Data mining; Datasets; Deep Learning; Dermatology; Diagnosis, Computer-Assisted; Feature extraction; Feature selection; Health aspects; Humans; Image acquisition; Inspection; Learning algorithms; Lesions; Localization; Machine learning; Medical imaging; Melanoma; Mortality; Neural networks; Neural Networks, Computer; Optimization techniques; Skin; Skin cancer; Skin diseases; Skin lesions; Transfer learning; United States > US
• ISSN: 1687-5265; 1687-5273; 1687-5273
• DOI: 10.1155/2021/9619079

In the USA, each year, almost 5.4 million people are diagnosed with skin cancer. Melanoma is one of the most dangerous types of skin cancer, and its survival rate is 5%. The development of skin cancer has risen over the last couple of years. Early identification of skin cancer can help reduce the human mortality rate. Dermoscopy is a technology used for the acquisition of skin images. However, the manual inspection process consumes more time and required much cost. The recent development in the area of deep learning showed significant performance for classification tasks. In this research work, a new automated framework is proposed for multiclass skin lesion classification. The proposed framework consists of a series of steps. In the first step, augmentation is performed. For the augmentation process, three operations are performed: rotate 90, right-left flip, and up and down flip. In the second step, deep models are fine-tuned. Two models are opted, such as ResNet-50 and ResNet-101, and updated their layers. In the third step, transfer learning is applied to train both fine-tuned deep models on augmented datasets. In the succeeding stage, features are extracted and performed fusion using a modified serial-based approach. Finally, the fused vector is further enhanced by selecting the best features using the skewness-controlled SVR approach. The final selected features are classified using several machine learning algorithms and selected based on the accuracy value. In the experimental process, the augmented HAM10000 dataset is used and achieved an accuracy of 91.7%. Moreover, the performance of the augmented dataset is better as compared to the original imbalanced dataset. In addition, the proposed method is compared with some recent studies and shows improved performance.