Multilayer vectorization to develop a deeper image feature learning model
D. Hemanand,
N. P. G. Bhavani,
Shahanaz Ayub,
Mohd Wazih Ahmad,
S. Narayanan,
Anandakumar Haldorai
Affiliations
D. Hemanand
Department of Computer Science and Engineering, S.A. Engineering College (Autonomous), Chennai, Tamil Nadu, India
N. P. G. Bhavani
Department of Electronic Instrumentation Systems Institute of ECE Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Chennai, Tamilnadu, India
Shahanaz Ayub
Electronics and Communication Engineering Department, Bundelkhand Institute of Engineering and Technology, Jhansi, Uttar Pradesh, India
Mohd Wazih Ahmad
Computer Science and Engineering, Adama Science and Technology University Adama, Adama, Ethiopia
S. Narayanan
Department of Information Technology, SRM Valliammai Engineering College, Kattankulathur, Chengalpattu, India
Anandakumar Haldorai
Department of Computer science and engineering, Sri Eshwar College of Engineering, Coimbatore, Tamil Nadu, India
Computer-Aided Diagnosis (CAD) approaches categorise medical images substantially. Shape, colour, and texture can be problem-specific in medical imagery. Conventional approaches rely largely on them and their relationship, resulting in systems that can't illustrate high-issue domain ideas and have weak prototype generalization. Deep learning techniques deliver an end-to-end model that classifies medical photos thoroughly. Due to the improved medical picture quality and short dataset size, this approach may have high processing costs and model layer restrictions. Multilayer vectorization and the Coding Network-Multilayer Perceptron (CNMP) are merged with deep learning to handle these challenges. This study extracts a high-level characteristic using vectorization, CNN, and conventional characteristics. The model's steps are below. The input picture is vectorized into a few pixels during preprocessing. These pixel images are delivered to a coding network being trained to create high-level classification feature vectors. Medical imaging fundamentals determine picture properties. Finally, neural networks combine the collected features. The recommended technique is tested on ISIC2017 and HIS2828. The model's accuracy is 91% and 92%.
