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The most dangerous form of cancer is breast cancer. This disease is life-threatening because of its aggressive nature and high death rates. Therefore, early discovery increases the patient’s survival. Mammography has recently been recommended as diagnosis technique. Mammography, is expensive and exposure the person to radioactivity. Thermography is a less invasive and affordable technique that is becoming increasingly popular. Considering this, a recent deep learning-based breast cancer diagnosis approach is executed by thermography images. Initially, thermography images are chosen from online sources. The collected thermography images are being preprocessed by Contrast Limited Adaptive Histogram Equalization (CLAHE) and contrasting enhancement methods to improve the quality and brightness of the images. Then, the optimal binary thresholding is done to segment the preprocessed images, where optimized the thresholding value using developed Rock Hyraxes Dandelion Algorithm Optimization (RHDAO). A newly implemented deep learning structure StackVRDNet is used for further processing breast cancer diagnosing using thermography images. The segmented images are fed to the StackVRDNet framework, where the Visual Geometry Group (VGG16), Resnet, and DenseNet are employed for constructing this model. The relevant features are extracted usingVGG16, Resnet, and DenseNet, and then obtain stacked weighted feature pool from the extracted features, where the weight optimization is done with the help of RHDAO. The final classification is performed using StackVRDNet, and the diagnosis results are obtained at the final layer of VGG16, Resnet, and DenseNet. A higher scoring method is rated for ensuring final diagnosis results. Here, the parameters present within the VGG16, Resnet, and DenseNet are optimized via the RHDAO to improve the diagnosis results. The simulation outcomes of the developed model achieve 97.05% and 86.86% in terms of accuracy and precision, respectively. The effectiveness of the designed methd is being analyzed via the conventional breast cancer diagnosis models in terms of various performance measures.

Diagnosis of thyroid disease is a most important cause in the field of medicinal research and it is a complex onset axiom. Secretion of Thyroid hormone plays a major role in the regulation of metabolism. Hence, it is very significant to predict thyroid disease in the initial stage, which is helpful for preventing more serious health complications due to thyroid cancer. The diagnostic accuracy of machine leaning-based approaches is greater but these techniques require large amounts of data for the diagnosis process. In the conventional approaches, the time needed for the prediction process is also high. Feature engineering is less investigated in conventional models and hence error produced during the prediction process is high. Hence, in this research work, a machine learning-aided thyroid disease prediction technique is designed to provide higher prediction accuracy and reliability. Initially, the thyroid data is gathered from the standard benchmark resources. Next, the data transformation process is carried out to make the data usable for analysis and visualization. After, the features are extracted using Principal Component Analysis (PCA), “One-Dimensional Convolutional Neural Network Model (1DCNN). Moreover, the statistical features are also extracted for getting more relevant information from the data. The three sets of features such as PCA-based, 1DCNN-based and statistical are concatenated and fed to the “optimal weighted feature selection” process, where the optimal features and weights are tuned by an Improved Archimedes Optimization Algorithm (IAOA). Next, the selected optimally fused features are given to the Ensemble Learning (EL) for predicting the thyroid diseases, where the EL with be suggested by incorporating stacking classifier, XGboost, and Multivariate regression classifier. Ensembling of three different classifiers provides higher thyroid disease prediction accuracy and it makes the decision about normal and abnormal classes. Here, the same IAOA is used for optimizing the parameters of every classifier. The investigational outcomes demonstrate that the proposed ensemble classifier provides higher performance than others. Experimental results prove that the thyroid prediction accuracy of the developed EL approach is 96.30%, precision is 99.67% and F1-score is 97.93%, which is more extensive than the state-of-the-art approaches.