ItemArticle

IoT-Integrated CNN Deep Learning for Automated Breast Cancer Detection and Diagnosis

Title
IoT-Integrated CNN Deep Learning for Automated Breast Cancer Detection and Diagnosis
Creator
Kalva, Yamini; Babu, R. Ganesh; Sindhu, V.; Pran, S. Gokul; Srilakshmi, Garaga; Kavitha, C.T.; Shanmugam, Sathish Kumar; Bhoopathy, V.
Description
Breast cancer continues to be a primary cause of death in women, requiring prompt and accurate diagnosis to enhance treatment results. Traditional diagnostic techniques depend on manual assessment, which leads to possible misclassification, significant inter-observer variability, and delays in decision-making. Current deep learning models, including CNNs, frequently experience feature loss, gradient declining and restricted adaptability to real-time data. To overcome these restrictions, we present a hybrid framework combining CNN and ResNet that merges deep learning-based feature extraction with real-time data collecting from IoT devices. The proposed approach utilises CNNs for preliminary feature extraction, ResNet for hierarchical learning with residual connections, and IoT for real-time patient monitoring and automatic notifications. The dataset undergoes preprocessing through normalisation, augmentation, and histogram equalisation to improve image quality and learning efficacy. The model is trained with cross-entropy loss and the Adam optimiser, guaranteeing stability and excellent performance. The evaluation results indicate a substantial enhancement compared to baseline models, with an accuracy of 97, an F1-score of 95.3, and a recall rate of 96.4%, exceeding traditional deep learning (90 accuracy) and CNN-based models (80% accuracy). The suggested model similarly minimises mistakes, with RMSE and MSE values declining to 1.2 and 1.6, respectively, signifying reduced misclassification rates. The inclusion of IoT facilitates instantaneous data transmission with little latency, hence improving clinical decision-making and minimising diagnostic delays. This advanced system facilitates automated and precise breast cancer detection, providing an innovative method for early diagnosis, optimised treatment planning, and improved patient outcomes, while ensuring data privacy and security through encryption and commitment to healthcare regulations. 2026 Yamini Kalva, R. Ganesh Babu, Sindhu V, S. Gokul Pran, Garaga Srilakshmi, Kavitha C T, Sathish Kumar Shanmugam and V. Bhoopathy. This open-access article is distributed under a Creative Commons Attribution (CC-BY) 4.0 license.
Source
Journal of Computer Science;Volume;22;Issue;4;pp.1218-1230
Date
01-01-2026
Publisher
Science Publications
Subject
Breast Cancer Detection; Convolutional Neural Network (CNN); Enhanced Cancer Classification; Health Automation; Internet of Things (IoT); Residual Neural Network (ResNet)
Coverage
Kalva Y., Sri Ramachandra Faculty of Engineering and Technology, Sri Ramachandra Institute of Higher Education and Research, Chennai, India; Babu R.G., Department of Electronics and Communication Engineering, Koneru Lakshmaiah Education Foundation, Andhra Pradesh, Guntur, India; Sindhu V., Department of Computer Science, Christ University, Karnataka, Bangalore, India; Pran S.G., Department of Computer Science and Engineering, Vel Tech Rangarajan Dr. SagunthalaR&D Institute of Science and Technology, Avadi, Chennai, India; Srilakshmi G., Department of Electronics and Communication Engineering, Aditya University, Andhra Pradesh, India; Kavitha C.T., Department of ECE, St. Joseph's Institute of Technology, Chennai, India; Shanmugam S.K., Department of EEE, M.Kumarasamy College of Engineering, Tamilnadu, Karur, India; Bhoopathy V., Department of Computer Science and Engineering, Sree Rama Engineering College, Tirupathi, India
Rights
All Open Access; Hybrid Gold Open Access
Relation
ISSN: 15493636;
Format
online
Language
English
Type
Article

Citation

Kalva, Yamini; Babu, R. Ganesh; Sindhu, V.; Pran, S. Gokul; Srilakshmi, Garaga; Kavitha, C.T.; Shanmugam, Sathish Kumar; Bhoopathy, V., “IoT-Integrated CNN Deep Learning for Automated Breast Cancer Detection and Diagnosis,” CHRIST (Deemed To Be University) Institutional Repository, accessed June 18, 2026, https://archives.christuniversity.in/items/show/23581.

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