TY  - JOUR
AU  - Kalva, Yamini 
AU  - Babu, R. Ganesh 
AU  - V., Sindhu 
AU  - Pran, S. Gokul 
AU  - Srilakshmi, Garaga 
AU  - T, Kavitha C 
AU  - Shanmugam, Sathish Kumar 
AU  - Bhoopathy, V. 
PY  - 2026
TI  - IoT-Integrated CNN Deep Learning for Automated Breast Cancer Detection and Diagnosis
JF  - Journal of Computer Science
VL  - 22
IS  - 4
DO  - 10.3844/jcssp.2026.1218.1230
UR  - https://thescipub.com/abstract/jcssp.2026.1218.1230
AB  - 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.