Multi-Class Classification of Skin Cancer Using Hybrid Inception-Residual Network


  • Ayoobkhan Mohamed Uvaze Ahamed Software Engineering, New Uzbekistan University, Tashkent, Uzbekistan
  • Venkata Rajesh Yella Assistant Professor, Department of Biotechnology, KoneruLakshmaiah Education Foundation, Green Fields, Vaddeswaram, Guntur, Andhra Pradesh, India
  • P. Vamsi Krishna Assistant Professor, School of Management, Malla Reddy University, Hyderabad, Telangana, India
  • Subramanian M. Assistant Professor, Department of Electronics and Instrumentation Engineering,Sai Leo Nagar,West Tambaram,Chennai, Tamilnadu, India
  • Amar Kumar Dey Assistant Professor, Department of Electronics and Telecommunications Engineering, Bhilai Institute of Technology, Durg (C.G), India
  • Santosh Kumar Behera Lecturer, JeyporeCollege of Pharmacy, Jeypore, Odisha, India


Skin Cancer, Medical Applications, Deep Learning, Data-Augmentation, Classification


Due to the emergence of medical applications, skin cancer is considered as one of the most common types of disease. Though, the occurrence of melanoma is seen in the form of cancer, it is complex to predict. When the lesions are found in the early phases, the survival rate of the patient may be increased. But, the existing automated models highly rely on the hand-crafted features and it is a complex process. Hence, this work aims to detect the multi-class of skin cancer using a deep learning (DL) model. The major processes like hair removal, optimal segmentation, feature extraction and classification processes are carried out. Initially, the hair removal process is carried out in the pre-processing stage. Then, for segmenting the affected region, optimal fuzzy clustering (OFC) is carried out. Finally, the enhanced DL model hybrid Inception-Residual network (Hybrid I-R network) is used for extracting and classifying the three stages of skin cancer. The Hybrid I-R network is the integration of an Inception-Residual network and dense network. The overall evaluation is carried out on the ISIC dataset and 5-fold cross-validation is carried out. The performances of the proposed hybrid model are compared with the other existing models and achieved better accuracy, recall and precision of 99.78%, 99.12% and 98.9% respectively. This shows that this model is more robust and reliable and efficiently utilized in skin cancer classification.


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How to Cite

Uvaze Ahamed, A. M. ., Yella, V. R. ., Krishna, P. V. ., M., S. ., Dey, A. K. ., & Behera, S. K. . (2023). Multi-Class Classification of Skin Cancer Using Hybrid Inception-Residual Network. International Journal of Intelligent Systems and Applications in Engineering, 12(2), 810–822. Retrieved from



Research Article