Neurological and brain-related cancers are one of the main causes of death worldwide. A commonly used tool in diagnosing these conditions is Magnetic Resonance Imaging (MRI), yet the manual evaluation of MRI images by medical experts presents difficulties due to time constraints and variability. This research introduces a novel, two-module computerized method aimed at increasing the speed and accuracy of brain tumor detection. The first module, termed the Image Enhancement Technique, utilizes a trio of machine learning and imaging strategies—adaptive Wiener filtering, neural networks, and independent component analysis—to normalize images and combat issues such as noise and varying low region contrast. The second module uses Support Vector Machines to validate the output of the first module and perform tumor segmentation and classification. Applied to various types of brain tumors, including meningiomas and pituitary tumors, our method exhibited significant improvements in contrast and classification efficiency. It achieved an average sensitivity and specificity of 0.991, accuracy of 0.989, and a Dice score (DSC) of 0.981. Furthermore, the processing time of our method, averaging at 0.43 seconds, was markedly lower compared to existing methods. These results underscore the superior performance of our approach over current state-of-the-art methods in terms of sensitivity, specificity, precision, and DSC. Future enhancements will seek to increase the robustness of the tumor classification method by employing a standardized approach across a suite of classifiers.

Nowadays, brain tumors have become a leading cause of mortality worldwide. The brain cells in the tumor grow abnormally and badly affect the surrounding brain cells. These cells could be either cancerous or non-cancerous types, and their symptoms can vary depending on their location, size, and type. Due to its complex and varying structure, detecting and classifying the brain tumor accurately at the initial stages to avoid maximum death loss is challenging. This research proposes an improved fine-tuned model based on CNN with ResNet50 and U-Net to solve this problem. This model works on the publicly available dataset known as TCGA-LGG and TCIA. The dataset consists of 120 patients. The proposed CNN and fine-tuned ResNet50 model are used to detect and classify the tumor or no-tumor images. Furthermore, the U-Net model is integrated for the segmentation of the tumor regions correctly. The model performance evaluation metrics are accuracy, intersection over union, dice similarity coefficient, and similarity index. The results from fine-tuned ResNet50 model are IoU: 0.91, DSC: 0.95, SI: 0.95. In contrast, U-Net with ResNet50 outperforms all other models and correctly classified and segmented the tumor region.

A brain tumor is a significant health concern that directly or indirectly affects thousands of people worldwide. The early and accurate detection of brain tumors is vital to the successful treatment of brain tumors and the improved quality of life of the patient. There are several imaging techniques used for brain tumor detection. Among these techniques, the most common are MRI and CT scans. To overcome the limitations associated with these traditional techniques, computer-aided analysis of brain images has gained attention in recent years as a promising approach for accurate and reliable brain tumor detection. In this study, we proposed a fine-tuned vision transformer model that uses advanced image processing and deep learning techniques to accurately identify the presence of brain tumors in the input data images. The proposed model FT-ViT involves several stages, including the processing of data, patch processing, concatenation, feature selection and learning, and fine tuning. Upon training the model on the CE-MRI dataset containing 5712 brain tumor images, the model could accurately identify the tumors. The FT-Vit model achieved an accuracy of 98.13%. The proposed method offers high accuracy and can significantly reduce the workload of radiologists, making it a practical approach in medical science. However, further research can be conducted to diagnose more complex and rare types of tumors with more accuracy and reliability.

This paper presents a comprehensive study on the classification of brain tumor images using five pre-trained vision transformer (ViT) models, namely R50-ViT-l16, ViT-l16, ViT-l32, ViT-b16, and ViT-b32, employing a fine-tuning approach. The objective of this study is to advance the state-of-the-art in brain tumor classification by harnessing the power of these advanced models. The dataset utilized for experimentation consists of a total of 4855 images in the training set and 857 images in the testing set, encompassing four distinct tumor classes. The performance evaluation of each model is conducted through an extensive analysis encompassing precision, recall, F1-score, accuracy, and confusion matrix metrics. Among the models assessed, ViT-b32 demonstrates exceptional performance, achieving a high accuracy of 98.24% in accurately classifying brain tumor images. Notably, the obtained results outperform existing methodologies, showcasing the efficacy of the proposed approach. The contributions of this research extend beyond conventional methods, as it not only employs cutting-edge ViT models but also surpasses the performance of existing approaches for brain tumor image classification. This study not only demonstrates the potential of ViT models in medical image analysis but also provides a benchmark for future research in the field of brain tumor classification.

Accurate classification of brain tumors is essential for effective medical diagnosis and treatment planning. Traditional approaches rely on convolutional neural networks (CNNs) for tumor detection, but they often suffer from high computational demands due to the large number of parameters. In this paper, we propose a novel approach for brain tumor classification using involutional neural networks (InvNets), which are designed to mitigate the parameter-intensive nature of CNNs. Unlike the spatial-agnostic and channel-specific convolution kernel, the involution kernel is location-specific and channel-agnostic. This location-specific operation allows the network to adapt to various visual patterns with respect to different spatial locations, enhancing its ability to capture intricate features within the medical images. Our study focuses on a four-class brain tumor classification problem, aiming to differentiate between different tumor types based on medical imaging data. In a comparative analysis, we demonstrate that conventional CNNs require over 4 million parameters, whereas our proposed InvNets require less than 0.2 million parameters, making them more efficient and resource-friendly. The evaluation of both CNNs and InvNets is carried out using standard performance matrices: accuracy, precision, recall, F1 score, and AUC-ROC values. Our findings reveal that the InvNets consistently outperform traditional CNNs. The InvNet architecture achieves an impressive 92% accuracy rate, showcasing its potential for accurate brain tumor classification. This improved accuracy, combined with the reduced parameter count, highlights the effectiveness of InvNets for medical image analysis tasks, especially in scenarios with limited computational resources.

Computed tomography (CT) is a key imaging technique in diagnostic radiology, providing highly sensitive and specific information. While its use has increased dramatically in recent years, the quantity and associated risks of radiation from CT scans present major challenges, particularly in paediatrics. The fundamental principles of radiation protection require that radiation quantities be as low as reasonably achievable and CT use must be justified, particularly for paediatric patients. CT radiation knowledge is a key factor in optimising and minimising radiation risk. The objective of this study was to analyse knowledge level, expertise, and competency regarding CT radiation dose and its hazards in paediatrics among radiologists in Saudi Arabian hospitals. A self-reported, multiple-choice questionnaire assessed the attitudes and opinions of radiologists involved in imaging studies using ionising radiation. Among the total respondents, 65% ± 13.5% had a good comprehension of the dangers of carcinogenicity to the patient resulting from CT scans, with 80% presuming that cancer risks were elevated. However, only 48.5%, 56.5%, and 65% of the respondents were aware of specific radiation risks in head, chest, and abdominal paediatric examinations, respectively. Regular, frequent, and specific training courses are suggested to improve the fundamental knowledge of CT radiation among radiologists and other physicians.