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Breast cancer is a significant health concern due to its aggressive nature and high mortality rates. Early detection is crucial to improving patient outcomes. Thermography, a non-invasive and cost-effective method, utilizes heat from the breast surface to detect abnormalities, with the Database for Mastology Research (DMR) leveraging infrared images for research purposes. Deep learning (DL), particularly convolutional neural networks (CNNs), shows promise in accurately classifying breast cancer images. However, CNNs face challenges with hyperparameters. To address these challenges, this article proposes a new DL model, the optimized DenseNet model (LFR-COA-DenseNet121-BC), incorporating a boosted metaheuristic algorithm called LFR-COA. This algorithm, a developed version of the Coati Optimization Algorithm (COA), integrates Random opposition-based learning (ROB), Brownian motion, and Lévy Flight (LF) schemes. The proposed model achieved impressive results, accurately classifying 99.97% of the test set. Comparison with established models such as VGG16, VGG19, DenseNet201, InceptionV3, Xception, and MobileNet revealed superior performance of the LFR-COA-DenseNet121-BC model, with 99.97% accuracy, 99.96% sensitivity, and 99.9% specificity. Further comparison with COA-DenseNet121 highlighted the superiority of LFR-COA-DenseNet-BC. In addition, LFR-COA was evaluated at the IEEE Congress on Evolutionary Computation held in 2022 (CEC 2022), and real-world medical scenarios showcased the effectiveness of LFR-COA compared to existing optimization algorithms. LFR-COA consistently outperformed the original COA algorithm and other well-known counterparts in various statistical, convergence, and diversity measures, affirming its efficacy in breast cancer classification. • CEC'22 test suite is utilized for verification of LFR-COA performance. • The LFR-COA algorithm optimizes various hyperparameters of the DenseNet121 model. • LFR-COA algorithm is proposed for solving global optimization problems. • LFR-COA-DenseNet121-BC: a new diagnostic model for breast cancer using DMR-IR dataset. • The proposed model surpasses other SOTA methods. [ABSTRACT FROM AUTHOR]

Thermography images are a helpful screening tool that can detect breast cancer by showing the body parts that indicate an abnormal change in temperature. Various segmentation methods are proposed to extract regions of interest from breast cancer images to enhance the classification. Many issues were solved using thresholding. In this paper, a new efficient version of the recent chimp optimization algorithm (ChOA), namely opposition-based Lévy Flight chimp optimizer (IChOA), was proposed. The original ChOA algorithm can stagnate in local optima and needs varied exploration with an adequate blending of exploitation. Therefore, the convergence is accelerated by improving the initial diversity and good exploitation capability at a later stage of generations. Opposition-based learning (OBL) is applied at the initialization phase of ChOA to boost its population diversity in the search space, and the Lévy Flight is used to enhance its exploitation. Moreover, the IChOA is applied to tackle the image segmentation problem using multilevel thresholding. The proposed method tested using Otsu and Kapur methods over a dataset from Mastology Research with Infrared Image (DMR-IR) database during the optimization process. Furthermore, compared against seven other meta-heuristic algorithms, namely Gray wolf optimization (GWO), Moth flame optimization (MFO), Whale optimization algorithm (WOA), Sine–cosine algorithm (SCA), Slap swarm algorithm (SSA), Equilibrium optimization (EO), and original Chimp optimization algorithm (ChOA). Results based on the fitness values of obtained best solutions revealed that the IChOA achieved valuable and accurate results in terms of quality, consistency, accuracy, and the evaluation matrices such as PSNR, SSIM, and FSIM. Eventually, IChOA obtained robustness for the segmentation of various positive and negative cases compared to the methods of its counterparts. • Improved Chimp Optimization Algorithm using Opposition-based Learning and Lévy Flight. • Evaluate IChOA to solve the Multi-level Thresholding Cancer Segmentation Imaging. • The efficiency of the algorithm is evaluated using Otsu and Kapur methods. • Verify the segmentation quality using the PSNR, SSIM, FSIM. • The quality of the segmentation results is better than other competitor algorithms. [ABSTRACT FROM AUTHOR]

Breast cancer is the second leading cause of death for women, so accurate early detection can help decrease breast cancer mortality rates. Computer-aided detection allows radiologists to detect abnormalities efficiently. Medical images are sources of information relevant to the detection and diagnosis of various diseases and abnormalities. Several modalities allow radiologists to study the internal structure, and these modalities have been met with great interest in several types of research. In some medical fields, each of these modalities is of considerable significance. This study aims at presenting a review that shows the new applications of machine learning and deep learning technology for detecting and classifying breast cancer and provides an overview of progress in this area. This review reflects on the classification of breast cancer utilizing multi-modalities medical imaging. Details are also given on techniques developed to facilitate the classification of tumors, non-tumors, and dense masses in various medical imaging modalities. It first provides an overview of the different approaches to machine learning, then an overview of the different deep learning techniques and specific architectures for the detection and classification of breast cancer. We also provide a brief overview of the different image modalities to give a complete overview of the area. In the same context, this review was performed using a broad variety of research databases as a source of information for access to various field publications. Finally, this review summarizes the future trends and challenges in the classification and detection of breast cancer. • Analysis the current research methodologies on deep learning and machine learning techniques. • Provide the deep learning for breast cancer using various modalities of medical imaging. • Provide the machine learning for breast cancer using various modalities of medical imaging. • Illustrate the modalities of medical imaging used for classifying breast cancer. • Present the datasets used in the classification models for medical images. [ABSTRACT FROM AUTHOR]

The COVID-19 is one of the most significant obstacles that humanity is now facing. The use of computed tomography (CT) images is one method that can be utilized to recognize COVID-19 in early stage. In this study, an upgraded variant of Moth flame optimization algorithm (Es-MFO) is presented by considering a nonlinear self-adaptive parameter and a mathematical principle based on the Fibonacci approach method to achieve a higher level of accuracy in the classification of COVID-19 CT images. The proposed Es-MFO algorithm is evaluated using nineteen different basic benchmark functions, thirty and fifty dimensional IEEE CEC'2017 test functions, and compared the proficiency with a variety of other fundamental optimization techniques as well as MFO variants. Moreover, the suggested Es-MFO algorithm's robustness and durability has been evaluated with tests including the Friedman rank test and the Wilcoxon rank test, as well as a convergence analysis and a diversity analysis. Furthermore, the proposed Es-MFO algorithm resolves three CEC2020 engineering design problems to examine the problem-solving ability of the proposed method. The proposed Es-MFO algorithm is then used to solve the COVID-19 CT image segmentation problem using multi-level thresholding with the help of Otsu's method. Comparison results of the suggested Es-MFO with basic and MFO variants proved the superiority of the newly developed algorithm. [ABSTRACT FROM AUTHOR]

Feature selection (FS) represents an optimization problem that aims to simplify and improve the quality of highly dimensional datasets through selecting prominent features and eliminating redundant and irrelevant data to classify results better. The goals of FS comprise dimensionality reduction and enhancing the classification accuracy in general, accompanied by great significance in different fields like data mining applications, pattern classification, and data analysis. Using powerful optimization algorithms is crucial to obtaining the best subsets of information in FS. Different metaheuristics, such as the Sooty Tern Optimization Algorithm (STOA), help to optimize the FS problem. However, such kind of techniques tends to converge in sub-optimal solutions. To overcome this problem in the STOA, an improved version called mSTOA is introduced. It employs the balancing exploration/exploitation strategy, self-adaptive of the control parameters strategy, and population reduction strategy. The proposed approach is proposed for solving the FS problem, but also it has been validated over benchmark optimization problems from the CEC 2020. To assess the performance of the mSTOA, it has also been tested with different algorithms. The experiments in terms of FS provide qualitative and quantitative evidence of the capabilities of the mSTOA for extracting the optimal subset of features. Besides, statistical analyses and no-parametric tests were also conducted to validate the result obtained by the mSTOA in optimization. • An enhanced algorithm called the mSTOA that employs three strategies is proposed. • mSTOA efficiency and performance are verified on several benchmarks. • CEC'20 test suite are used for algorithm validation. • mSTOA is proposed as an alternate feature selection approach. [ABSTRACT FROM AUTHOR]