An IoT-Based Framework and Ensemble Optimized Deep Maxout Network Model for Breast Cancer Classification

• Published in: Electronics (Basel) Vol. 11; no. 24; p. 4137
• Main Authors: Peta, Jyothi, Koppu, Srinivas
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.12.2022
• Subjects: Breast cancer; Classification; Computer-aided medical diagnosis; Datasets; Deep learning; Diagnosis; Electronic devices; Health care; Health care industry; Internet of Things; Investment analysis; Iterative methods; Machine learning; Methods; Model accuracy; Mutation; Optimization; Optimization algorithms; Patients; Psychology; Sensors; Womens health; India
• ISSN: 2079-9292; 2079-9292
• DOI: 10.3390/electronics11244137

Internet of Things (IoT) plays an essential role in the area of the healthcare system. IoT devices provide information about patients in the healthcare monitoring framework. Moreover, patients can examine their health with smart devices and hence IoT is a major factor in all aspects of the health care management system. Breast cancer is a deadly cancer in women and the detection of this disease at the primary stage increases the survival rate. Due to the computational complexity associated with acquiring features, classification results generated from the existing methods are unsatisfactory and hence it is important to design a method using deep learning concepts for classifying cancer disease. An efficient and robust classification model named Student Psychology Whale Optimization-based Deep maxout network with optimization (SPWO-based Deep maxout network) classifies breast cancer disease. The advantage of using a Deep maxout network is that it effectively learns intrinsic features from the data. The weight factor of the deep learning model is updated with respect to iteration based on the fitness measure that in turn results in higher results by acquiring a minimal error value. However, the proposed model obtains outstanding accuracy, sensitivity, and specificity in terms of testing with the values of 0.931, 0.953, and 0.915 with 100 nodes.