The study of brain disorders requires accurate tissue segmentation of magnetic resonance (MR) brain images which is very important for detecting tumors, edema, and necrotic tissues. Segmentation of brain images, especially into three main tissue types: Cerebrospinal Fluid (CSF), Gray Matter (GM), and White Matter (WM), has important role in computer aided neurosurgery and diagnosis. Brain images mostly contain noise, intensity inhomogeneity, and weak boundaries. Therefore, accurate segmentation of brain images is still a challenging area of research. This paper presents a review of fuzzyc-means (FCM) clustering algorithms for the segmentation of brain MR images. The review covers the detailed analysis of FCM based algorithms with intensity inhomogeneity correction and noise robustness. Different methods for the modification of standard fuzzy objective function with updating of membership and cluster centroid are also discussed.

Color image segmentation is very important for separating an object of interest from given input image. For cotton leaf disease detection, an infected part of leaf must be separated out for further classification. This paper proposed a technique for cotton leaf diseases detection and classification using the concept of roughness measure and simple linear iterative clustering. An optimum number of superpixel group are formed using roughness measure for extracting region of interest of cotton leaf. Gray level co-occurrence matrix features are extracted from detected region. Support vector machine, a supervised machine learning algorithm is used to classify cotton leaf into four different categories as Alternaria diseases, Bacterial diseases, White flies, and Healthy cotton leaf. Proposed algorithms demonstrated the average classification accuracy of 94% with the available database.

Non-contact-based baby monitoring system using image processing is proposed in this paper which is used for proper safety and monitoring the activity of baby by their busy parents. The system detects the motion, crying and present position of the baby. If any abnormal action is detected, then the system sends a message in the form of text and images of baby to the particular user through email. Raspberry Pi B+ module is used to process the videos taken by pi camera, MIC is used for crying detection and image processing is used for detection of real-time motion of babies and boundary condition of the bed. The system required to first install OS Raspbian, and all the other packages like OpenCV, Numpy and Virtual environment. Face detection algorithm is trained using Haar classifier for positive face images and negative nonface images. This system will help in decreasing the chances of the baby’s falling from the bed. Also, this system can be used in hospitals while baby is sleeping where the stress among the nurses will be reduced.

Background: Alzheimer’s disease (AD) is a progressive neurodegenerative disorder and is the most common cause of dementia. Early diagnosis of Alzheimer’s disease is critical for better management and treatment outcomes, but it remains a challenging task due to the complex nature of the disease. Clinical data, including a range of cognitive, functional, and demographic variables, play a crucial role in Alzheimer’s disease classification. Also, challenges such as data imbalance and high-dimensional feature sets often hinder model performance. Objective: This paper aims to propose a computationally efficient, reliable, and transparent machine learning-based framework for the classification of Alzheimer’s disease patients. This framework is interpretable and helps medical practitioners learn complex patterns in patients. Method: This study addresses these issues by employing boosting algorithms, for enhanced classification accuracy. To mitigate data imbalance, a random sampling technique is applied, ensuring a balanced representation of Alzheimer’s and healthy cases. Extensive feature analysis was conducted to identify the most impactful clinical features followed by feature reduction techniques to focus on the most informative clinical features, reducing model complexity and overfitting risks. Explainable AI tools, such as SHAP, LIME, ALE, and ELI5 are integrated to provide transparency into the model’s decision-making process, highlighting key features influencing the classification and allowing clinicians to understand and trust the key features driving the predictions. Results: This approach results in a robust, interpretable, and clinically relevant framework for Alzheimer’s disease diagnosis. The proposed approach achieved the best accuracy of 95%, demonstrating its effectiveness and potential for reliable early diagnosis of Alzheimer’s disease. Conclusions: This study demonstrates that integrating ensemble learning algorithms and explainable AI, while using a balanced dataset with feature selection, improves quantitative results and interpretability. This approach offers a promising method for early and better-informed clinical decisions.

Background/Objectives: Stroke stands as a prominent global health issue, causing con-siderable mortality and debilitation. It arises when cerebral blood flow is compromised, leading to irreversible brain cell damage or death. Leveraging the power of machine learning, this paper presents a systematic approach to predict stroke patient survival based on a comprehensive set of factors. These factors include demographic attributes, medical history, lifestyle elements, and physiological metrics. Method: An effective random sampling method is proposed to handle the highly biased data of stroke. The stroke pre-diction using optimized boosting machine learning algorithms is supported with explainable AI using LIME and SHAP. This enables the models to discern intricate data patterns and establish correlations between selected features and patient survival. Results: The performance of three boosting algorithms is studied for stroke prediction, which include Gradient Boosting (GB), AdaBoost (ADB), and XGBoost (XGB) with XGB achieved the best outcome overall with a training accuracy of 96.97% and testing accuracy of 92.13%. Conclusions: Through this approach, the study seeks to uncover actionable insights to guide healthcare practitioners in devising personalized treatment strategies for stroke patients.