An Efficient Multi-Scale Convolutional Neural Network Based Multi-Class Brain MRI Classification for SaMD

Abstract

A brain tumor is the growth of abnormal cells in certain brain tissues with a high mortality rate; therefore, it requires high precision in diagnosis, as a minor human judgment can eventually cause severe consequences. Magnetic Resonance Image (MRI) serves as a non-invasive tool to detect the presence of a tumor. However, Rician noise is inevitably instilled during the image acquisition process, which leads to poor observation and interferes with the treatment. Computer-Aided Diagnosis (CAD) systems can perform early diagnosis of the disease, potentially increasing the chances of survival, and lessening the need for an expert to analyze the MRIs. Convolutional Neural Networks (CNN) have proven to be very effective in tumor detection in brain MRIs. There have been multiple studies dedicated to brain tumor classification; however, these techniques lack the evaluation of the impact of the Rician noise on state-of-the-art deep learning techniques and the consideration of the scaling impact on the performance of the deep learning as the size and location of tumors vary from image to image with irregular shape and boundaries. Moreover, transfer learning-based pre-trained models such as AlexNet and ResNet have been used for brain tumor detection. However, these architectures have many trainable parameters and hence have a high computational cost. This study proposes a two-fold solution: (a) Multi-Scale CNN (MSCNN) architecture to develop a robust classification model for brain tumor diagnosis, and (b) minimizing the impact of Rician noise on the performance of the MSCNN. The proposed model is a multi-class classification solution that classifies MRIs into glioma, meningioma, pituitary, and non-tumor. The core objective is to develop a robust model for enhancing the performance of the existing tumor detection systems in terms of accuracy and efficiency. Furthermore, MRIs are denoised using a Fuzzy Similarity-based Non-Local Means (FSNLM) filter to improve the classification results. Different evaluation metrics are employed, such as accuracy, precision, recall, specificity, and F1-score, to evaluate and compare the performance of the proposed multi-scale CNN and other state-of-the-art techniques, such as AlexNet and ResNet. In addition, trainable and non-trainable parameters of the proposed model and the existing techniques are also compared to evaluate the computational efficiency. The experimental results show that the proposed multi-scale CNN model outperforms AlexNet and ResNet in terms of accuracy and efficiency at a lower computational cost. Based on experimental results, it is found that our proposed MCNN2 achieved accuracy and F1-score of 91.2% and 91%, respectively, which is significantly higher than the existing AlexNet and ResNet techniques. Moreover, our findings suggest that the proposed model is more effective and efficient in facilitating clinical research and practice for MRI classification.

Dendrimer: An update on recent developments and future opportunities for the brain tumors diagnosis and treatment

“…Magnetic resonance imaging (MRI) technique was the first one utilised to detect and diagnose brain tumour lesions ( Khan et al, 2020 ; Almalki et al, 2022 ; Wu et al, 2022 ; Yazdan et al, 2022 ). Meanwhile, gadolinium (Gd)-chelated diagnostic agents are extensively utilized as contrast agents for tumor imaging ( Rodríguez-Galván et al, 2020 ).…”

“…Therefore, molecular imaging techniques would be useful for forecasting therapy response, prudently segmenting patients, measuring biodistribution, and determining the drug release profile (Bernsen et al, 2013;Li et al, 2021). Magnetic resonance imaging (MRI) technique was the first one utilised to detect and diagnose brain tumour lesions (Khan et al, 2020;Almalki et al, 2022;Wu et al, 2022;Yazdan et al, 2022). Meanwhile, gadolinium (Gd)-chelated diagnostic agents are extensively utilized as contrast agents for tumor imaging (Rodríguez-Galván et al, 2020).…”

A Healthcare Paradigm for Deriving Knowledge Using Online Consumers’ Feedback

“…Data analysis tools evaluate various data types and run relevant analyses to gain insights from data records. This is essential when it comes to translating raw patient data into useful information, which can be used to support the decision-making in healthcare organizations [ 19 , 20 ]. Delen [ 21 ] proposed a simple classification of analytics that distinguishes three types of analytics: descriptive, predictive, and prescriptive, each defined by the data type and the objective of the study.…”

Brain Tumor Detection using Integrated Learning Process Detection (ILPD)

“…Compare with all the existing models, the proposed approach achieved the 98.6% of accuracy. Yazdan SA et al, [27][28][29][30][31][32] proposed the automated diagnostic approach called as Multi-Scale CNN (MSCNN) to classify [32][33][34][35][36] The VGG contains very small convolutional filters. VGG-19 contains 19 convolutional layers and three are fully connected layers (see Fig.…”