Melanoma Skin Cancer Detection using a CNN-Regularized Extreme Learning Machine (RELM) based Model
- Title
- Melanoma Skin Cancer Detection using a CNN-Regularized Extreme Learning Machine (RELM) based Model
- Creator
- Subramanian M.; Walid M.A.A.; Mallick S.P.; Rastogi R.; Chauhan A.; Vidya A.
- Description
- Recent years have brought a heightened awareness of skin cancer as a potentially fatal type of human disease. While all three forms of skin cancer - Melanoma, Basal, and Squamous are terrifying, Melanoma is the most erratic. Melanoma cancer is curable if caught at an early stage. Multiple current systems have demonstrated that computer vision can play a significant role in medical image diagnosis. This study suggests a new approach to picture categorization that can help convolutional neural networks train more quickly (CNN). CNN has seen widespread use in multiclass image classification datasets, but its poor learning performance for huge volumes of data has limited its usefulness. On the other hand, whereas Regularized Extreme Learning Machine (RELM) are capable of rapid learning and have strong generalizability to improve their recognized accuracy quickly. This study introduces a novel CNN-RELM, a novel classifier that integrates convolutional neural networks with regularized extreme learning machines. CNN-RELM begins by training a Convolutional Neural Network (CNN) through the gradient descent technique until the desired learning and target accuracy is achieved. This approach outperforms the CNN and RELM model with an accuracy of around 98.6%. 2023 IEEE.
- Source
- Proceedings of the 2023 2nd International Conference on Electronics and Renewable Systems, ICEARS 2023, pp. 1239-1245.
- Date
- 2023-01-01
- Publisher
- Institute of Electrical and Electronics Engineers Inc.
- Subject
- Convolutional Neural Network (CNN); Extreme Learning Machine (ELM) and Regularized Extreme Learning Machine (KELM).
- Coverage
- Subramanian M., Sri Sairam Engineering College, Department of Electronics and Instrumentation Engineering, Tamilnadu, Chennai, India; Walid M.A.A., Khulna University of Engineering and Technology (KUET), Khulna, 9203, Bangladesh; Mallick S.P., Koneru Lakshmaiah Education Foundation, Kl University, Department of Biotechnology, Andhra Pradesh Green Fields, India; Rastogi R., Nielit, Uttara Pradesh, Gorakhpur, India; Chauhan A., Deemed to Be University, Christ, Department of Life Sciences, Karnataka, Bengaluru, India; Vidya A., K. Ramakrishnan College of Engineering, Department of Electronics and Communication Engineering, Tamilnadu, India
- Rights
- Restricted Access
- Relation
- ISBN: 979-835034664-0
- Format
- Online
- Language
- English
- Type
- Conference paper
Collection
Citation
Subramanian M.; Walid M.A.A.; Mallick S.P.; Rastogi R.; Chauhan A.; Vidya A., “Melanoma Skin Cancer Detection using a CNN-Regularized Extreme Learning Machine (RELM) based Model,” CHRIST (Deemed To Be University) Institutional Repository, accessed February 24, 2025, https://archives.christuniversity.in/items/show/19969.