Smart IoT in Breast Cancer Detection Using Optimal Deep Learning

• Published in: Journal of digital imaging Vol. 36; no. 4; pp. 1489 - 1506
• Main Authors: Majji, Ramachandro, G., Om Prakash P., Rajeswari, R., R., Cristin
• Format: Journal Article
• Language: English
• Published: Cham Springer International Publishing 01.08.2023; Springer Nature B.V
• Subjects: Algorithms; Artificial neural networks; Breast; Breast cancer; Breast Neoplasms - diagnostic imaging; Data augmentation; Deep Learning; Energy; Feature extraction; Feedback; Female; Flow cytometry; Health care; Health services; Homogeneity; Humans; Image enhancement; Image quality; Imaging; Internet of Things; Latency; Machine learning; Mammography; Medical imaging; Medicine; Medicine & Public Health; Network latency; Neural networks; Radiology; Search algorithms; Sensitivity; Crow Search Algorithm; And Shepherd Convolutional Neural Network; Breast cancer; Feedback Artificial Tree; Internet of Things
• ISSN: 1618-727X; 0897-1889; 1618-727X
• DOI: 10.1007/s10278-023-00834-9

IoT in healthcare systems is currently a viable option for providing higher-quality medical care for contemporary e-healthcare. Using an Internet of Things (IoT)–based smart healthcare system, a trustworthy breast cancer classification method called Feedback Artificial Crow Search (FACS)–based Shepherd Convolutional Neural Network (ShCNN) is developed in this research. To choose the best routes, the secure routing operation is first carried out using the recommended FACS while taking fitness measures such as distance, energy, link quality, and latency into account. Then, by merging the Crow Search Algorithm (CSA) and Feedback Artificial Tree, the produced FACS is put into practice (FAT). After the completion of routing phase, the breast cancer categorization process is started at the base station. The feature extraction step is then introduced to the pre-processed input mammography image. As a result, it is possible to successfully get features including area, mean, variance, energy, contrast, correlation, skewness, homogeneity, Gray Level Co-occurrence Matrix (GLCM), and Local Gabor Binary Pattern (LGBP). The quality of the image is next enhanced through data augmentation, and finally, the developed FACS algorithm’s ShCNN is used to classify breast cancer. The performance of FACS-based ShCNN is examined using six metrics, including energy, delay, accuracy, sensitivity, specificity, and True Positive Rate (TPR), with the maximum energy of 0.562 J, the least delay of 0.452 s, the highest accuracy of 91.56%, the higher sensitivity of 96.10%, the highest specificity of 91.80%, and the maximum TPR of 99.45%.