# Special Issue: Artificial Intelligence Technology in Medical Image Analysis
###### Abstract  
The proposed method for medical imaging is a novel and efficient method for medical imaging. The proposed method is based on a novel and efficient method for medical imaging. different versions of deep convolutional neural networks (DCNNs), coupled with a support vector machine (SVM) classifier. The purpose is to classify histopathological images of breast cancer into eight specific classes--four benign and four malignant ones. The methodology harnesses the strengths of DCNNs to extract a highly predictive multi-scale pooled image feature representation, from breast cancer images at four distinct resolutions. These representations are then subjected to classification using SVM. The proposed CNN (convolutional neural network) architecture achieves an impressive 90% accuracy for eight-class breast cancer classification. In the second paper, [PERSON] et al. introduce a method for estimating age using T1-weighted two-dimensional brain magnetic resonance (MR) images, involving a cohort of 1000 subjects aged between 5 and 79 years. The proposed approach employs a regression model based on the ResNet-50 architecture, which predicts the chronological age of unknown brain MR images using its training on images corresponding to the respective ages. The study's findings reveal a comparable level of correlation to related research, indicating the feasibility of age estimation across a broad age spectrum with enhanced accuracy. This suggests that the proposed method holds promise for accurate age estimation, even in diverse age groups, when utilizing T1-weighted two-dimensional brain MR imaging. In the third paper, [PERSON] et al. report on the development and evaluation of a quality assurance (QA) system for chest X-ray images, based on deep learning. The QA system includes three classification models and one regression model, developed using a dataset of 23,000 curated images from various sources. The classification method addresses issues such as image orientation correction, left-right reversal, and the estimation of the patient's body position. The regression method focuses on correcting the image angle. Several CNN models are compared, using five cross-fold evaluations. The overall accuracy of the QA system is assessed using clinical images, and the mean correction time of the system is also measured. The deep learning-based QA system demonstrates efficient and accurate corrections of chest X-ray images. The utilization of ResNet-50 for classification and the classical CNN for regression proves to be effective, ensuring precise adjustments for various aspects of image quality. In the fourth paper, [PERSON] et al. focus on the development and evaluation of deep-learning algorithms designed to automatically recommend orthotic insoles for individuals experiencing foot pain. The input data, gathered from 838 patients, includes details such as the resting calcaneal stance position, pelvic elevation, pelvic tilt, and pelvic rotation. The target data encompasses the foot posture index for the modified root technique, as well as the requirement for interventions such as heel lift, entire lift, and specific supports like lateral wedge, medial wedge, and calcaneocuboid arch. The deep-learning models developed therein, coupled with statistical validation, demonstrate their proficiency in automatically prescribing orthotic insoles for patients with foot pain, achieving commendable accuracy across various parameters. In the fifth paper, [PERSON] et al. explore various CNN architectures for their effective integration into the automated analysis of histopathological whole slide images. This advancement enhances the capability to detect early stages of diseases, including cancer, thereby contributing to improved health monitoring strategies. Moreover, the predictive abilities of these systems provide opportunities for anticipating and controlling the spread of global diseases, enabling preliminary analyses and viable solutions.