## 2 Motivation
### Literature Survey
As a result, the proposed model achieved the highest comparative analysis in the performance metrics. [PERSON] et al. [30] presented an artificial magnetic conductor (AMC)-based antenna for detecting breast tumours. In comparison to previously produced sensors, this sensor functions from a single feed point. System costs are reduced by using single tasking, point-of-care sensors. It additionally diminishes the intricacy of multi-tasking point-of-care information handling brought about by a few point-of-care sensors. To recognize a disease or harmful cancer cells in the breast, a head part examination and explicit retention rate (SAR) are used as boundaries. For each layer of the 3D breast model, SAR values were generated. The results revealed that SAR values vary depending on tumour size and location. [PERSON] et al. [31] proposed a hybrid energy-efficient model to monitor cardiovascular symptoms at patient homes in a smart city. The proposed method uses sensors to capture the electrocardiogram (ECG) and perform the analysis of captured data. The proposed system produces better results by reducing energy usage, latency, and network utilization. [PERSON] et al. [32] presented an ANN efficient framework to identify the prognosis of ovary and breast cancer defects by using ultrasound images. Here, the proposed method concentrated on image features enhancement techniques, namely the histogram of oriented gradients (HOG) and fusion techniques. These methods worked to enhance the image features and are applied for better outcomes. [PERSON] et al. [33] introduced a systematic review to examine the CAD systems for breast cancer detection. This review covers all CAD phases, including pre-processing, segmentation, feature extraction, feature selection, and classification. The state of the recently developed models is presented in Table 1. \begin{table}
\begin{tabular}{p{56.9 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Reference** & **Technique Name** & **Dataset** & **Metrics** \\ \hline
[21] & CNN (Convolutional Neural network), SVM (Support Vector Machine) & Mammographic image analysis-society (MIAS) dataset & Accuracy—98.96\%, sensitivity—97.83\%, specificity—99.13\%, precision—97.35\%, F-score—97.66\%, AUC—0.995 & Accuracy—94.01, Recall—72.30, Precision—81.68. \\ \hline
[24] & AlexNet, VGG-16 and VGG-19 & BreakHis dataset. & Accuracy—94.01, Recall—72.30, Precision—81.68. \\ \hline
[34] & CNN (Convolutional Neural Network), RDNN (Recurrent Dynamic Neural Network) & ImageNet dataset & accuracy of 91.3\% \\ \hline
[25] & Deep Neural Network ResNet—18 & ImageNet dataset, BreakHis dataset. & Accuracy—92.03\%. \\ \hline
[23] & K-means Clustering & Public dataset Haberman’s survival (HS) dataset and Human activity recognition(HAR) dataset & Precision—92.78\%, F-measure—94.73\%. \\ \hline
[35] & PUF (Physical Unclonable Function) & Simulation & Accuracy—98.96\%, sensitivity—97.83\%, specificity—99.13\% \\ \hline
[22] & CNN (Convolutional Neural Network), KNN (K-Nearest Neighbors), ANN (Artificial Neural Network), SVM (Support Vector Machine) & Wisconsin Diagnostic Breast Cancer (WDBC) dataset & Accuracy—96.5\%, Specificity—95.7\%, F1—score—97\%, Recall—97\%. \\ \hline
[36] & CNN (Convolutional Neural Network), PSO (particle Swarm Optimization), ANN (Artificial Neural Network) & public training datasets & Accuracy—89.84\%. \\ \hline
[37] & Grasshopper Optimization & Mammographic Image Analysis Society Digital Mammogram Database and Digital Database for Screening Mammography breast cancer database & Sensitivity—96\%, Specificity—93\%, PPV—85\%, NPV—97\%, Accuracy—92\%, F-measure—94.73\%. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Parameters utilized for the experiment.