Background: Accurate classification of brain tumors in medical images is vital for effective diagnosis and treatment planning, which improves the patient’s survival rate. In this paper, we investigate the application of Convolutional Neural Networks (CNN) as a powerful tool for enhancing diagnostic accuracy using a Magnetic Resonance Imaging (MRI) dataset. Method: This study investigates the application of CNNs for brain tumor classification using a dataset of Magnetic Resonance Imaging (MRI) with a resolution of 200 × 200 × 1. The dataset is pre-processed and categorized into three types of tumors: Glioma, Meningioma, and Pituitary. The CNN models, including the Classic layer architecture and the ResNet50 architecture, are trained and evaluated using an 80:20 training-testing split. Results: The results reveal that both architectures accurately classify brain tumors. Classic layer architecture achieves an accuracy of 94.55%, while the ResNet50 architecture surpasses it with an accuracy of 99.88%. Compared to previous studies and 99.34%, our approach offers higher precision and reliability, demonstrating the effectiveness of ResNet50 in capturing complex features. Conclusions: The study concludes that CNNs, particularly the ResNet50 architecture, exhibit effectiveness in classifying brain tumors and hold significant potential in aiding medical professionals in accurate diagnosis and treatment planning. These advancements aim to further enhance the performance and practicality of CNN-based brain tumor classification systems, ultimately benefiting healthcare professionals and patients. For future research, exploring transfer learning techniques could be beneficial. By leveraging pre-trained models on large-scale datasets, researchers can utilize knowledge from other domains to improve brain tumor classification tasks, particularly in scenarios with limited annotated data.

Face landmarking is a primary goal of many projects that lead to face-preparing activities, like biometric recognition and mental state comprehension. Despite the inherent diversity of faces, the topic has proven to be extremely difficult due to a wide range of perplexing variables, such as position, expression, illumination, and occlusions. The aim of the HASAAM integrated fitting model is to find new solutions for the feature identification issue by combining the strengths of the Active Shape Model (ASM) and Active Appearance Model (AAM) to provide unique findings on the feature detection problem. In the first step, the ASM was used to identify the external shape landmarks of the face, and in the second stage, the AAM was used to identify the interior form landmarks. Then, the two kinds of landmarks are combined to generate the full face landmark. One issue with ASM is that it can’t produce an optimal global result since it will unavoidably converge to neighborhood minima. Nonetheless, ASM produces precise fitting results for the face’s exterior features, which need essential gradient values. In order to prevent AAM from fitting in order to address the local minima problem, ASM was used to identify these exterior landmarks of the face. The trials were run using the MORPH and LFPW datasets, which are available to the general public. In comparison to other techniques like ASM and AAM, the proposed hybrid model experiment result demonstrated effectiveness in extracting facial features with error rates of 2.2628% for the LFPW database and 2.7174% for the MORPH database. This better result helps to compensate for variations in shape and texture. The suggested hybrid method may be expanded to recognize gender, estimate head posture, estimate gaze, find facial feature points in the presence of significant pose fluctuations, and detect facial expressions.

Human age recognition becomes increasingly important due to its beneficial employments alongside security and computer applications. Age prediction from face picture has a lot of challenges, such as insufficiency of training data and uncontrollable situation. In this research, we address these critical issues by introducing an improved age prediction algorithm using Active Appearance Models (AAM) and three classifiers, Support Vector Machine (SVM), K-Nearest Neighbor (KNN), and Support Vector Regression (SVR) to improve the precision of age prediction based on the present methods. In this algorithm, the traits of the facial pictures are explicated as traits vectors by AAM model, and the classifiers are utilized to estimate the age. We were able to recognize that the accuracy of SVR algorithm is better than the accuracy of KNN and SVM classifiers.

Human facial feature extraction plays a critical role in various applications, including biorobotics, polygraph testing, and driver fatigue monitoring. However, many existing algorithms rely on end-to-end models that construct complex classifiers directly from face images, leading to poor interpretability. Additionally, these models often fail to capture dynamic information effectively due to insufficient consideration of respondents' personal characteristics. To address these limitations, this paper evaluates two prominent approaches: the constrained local model (CLM), which accurately extracts facial features depending on patch experts, and the active shape model (ASM), designed to simultaneously extract the appearance and shape of an object. We assess the performance of these models on the MORPH dataset using point to point error as evaluation metrics. Our experimental results demonstrate that the CLM achieves higher accuracy, while the ASM exhibits better efficiency. These findings provide valuable insights for selecting the appropriate model based on specific application requirements.