Brain Tumour Classification using Deep Learning with Residual Attention Network: A Comparative Study

The brain tumor is a devastating disease, which shortens the life expectancy quite a bit in their highest grade. We aim to present a multiclass brain tumor classification algorithm using deep networks convolution neural networks with loss function as the binary cross-entropy and optimizer as Adam and ResNet50 on multiple datasets of Magnetic Resonance Images. Deep learning has earned a lot of development in the medical field in the last few years. MRI has been a constructive tool for medical research in current years and has many applications like brain tumor detection. In the detection of tumors, MRI images of the brain were classified as tumorous or non-tumorous using deep network CNN. In the classification, contrast-enhanced MR images are used to classify the images into three types of brain tumor: pituitary, glioma, and meningioma. By making use of this, we can identify and give the required medication to the patients.KeywordsBrain tumorDeep learningCNNFigshareTransfer learning

Brain tumor is generally structured by the subtle summation of anomalous cells because it categorized as a mass of tissue, and it is very crucial to categorize brain tumors from Magnetic Resonance Imaging (MRI) for diagnosis purpose. The investigation of brain tumors by humans is a routine process for detecting and classifying MRI brain tumors. Due to the low contrast of the images, poor boundaries and noises, the image classification techniques provide poor performance in medical images. Hence, a new classification model for detecting brain tumors is needed to solve the critical challenges in existing approaches. This decreases human intervention while making decisions about brain tumors. Therefore, a new brain tumor classification model is investigated to solve the challenges in existing approaches with deep learning. The proposed framework consists of two main different phases (i) classifying normal and abnormal images and (b) Segmentation and severity classification of abnormal images. The first step is comprised of pre-processing and classification. Firstly, the required images are gathered from the benchmark databases. These collected images are undergone pre-processing, which is done by filtering methods and Contrast Limited Adaptive Histogram Equalization (CLAHE). Subsequently, the pre-processed image is fed into the new classification model as a Transformer-based Multi-scale Residual Attention Network (TMRAN), where the normal and abnormal images are obtained. While in the second case, the classified abnormal image is considered as the input for the segmentation process. The segmented image is obtained by Convolutional Neural Network (CNN), Modified UNet and UNet, in turn the parameters in UNet is optimized by using the Modified Controlling Parameters-based African Vultures Optimization Algorithm (MCP-AVOA), termed as Modified UNet. At the final stage, these segmented images are applied as input to the novel TMRAN model, where the hyper parameters are tuned optimally using improved MCP-AVOA in order to acquire the optimal severity classified results. The performance is validated using diverse measures and compared over other conventional methodologies. Thus, the findings elucidate that the proposed model attains higher classification results.

Automatic defect detection is of great significance to the production process of modern industries, which will affect the product appearance and quality. And a great number of economic loss may be caused due to the defects in the industrial production process. Traditional manual defect inspection method is labor-intensive and time-consuming with subjective factors, and the detection performance is random and uncertain. Machine learning has shown a good detection ability on small-scale samples, but the detect detection task against with poor contrast, weak texture, etc., will affect the effective feature representation. To address the detection task of steel surface defects, combined with the strong context extraction ability of deep learning, an accurate deep defect classification network is proposed in the paper to provide an end-to-end detection scheme. Fused with the residual network (Resnet50) and spatial attention block, a residual attention network is proposed for effective feature representation, which could make the classification network better focus on the defect areas. Meanwhile, due to the scale information change among different defects, a multi-scale context fusion (MCF) block is proposed for effective multi-scale feature extraction, which is conducive for multi-scale object detection. Experimental results on public defect data set show that the proposed defect detection network could acquire a superior classification performance compared with some typical classification networks.

Brain tumor is the phenomenal growth of abnormal tissues in the human brain. A malignant tumor is known as a cancerous cell, and it is the major cause of death among people across the globe. Nowadays, the detection and classification of brain tumors from Magnetic Resonance Images (MRI) is a very crucial task. This chapter addresses various computing methods such as Edge Detection (ED), feature extraction, and classification techniques for the detection and classification of brain tumor regions from MRI datasets. Initially, MRI images are collected, and then various pre-processing steps such as filtering and edge detection are applied. Then, different edge detection methods such as Sobel ED, Robert’s ED, Prewitt ED, Canny ED, Laplacian ED, and Laplacian of Gaussian (LoG) with sigma 3 are applied for the MRI datasets. Subsequently, segmentation techniques are applied for detecting tumor regions from MRI, and essential features are extracted using Discrete Wavelet Transform (DWT) method. Otsu’s thresholding and K-means clustering segmentation methods are used for the investigation. Further, the support vector machines (SVMs), Naïve Bayes (NB), K-nearest neighbors (KNN), Back Propagation Neural Network (BPNN), and feedforward neural networks (FFNN) classifiers are employed for the MRI classification purpose. The experiments are 146conducted using the brain tumor dataset in the MATLAB 2019a software environment. The experimental results are analyzed in multiple dimensions, and it shows that the SVM with Otsu’s thresholding method exhibits better performance with 86.11% accuracy during the classification.

In recent years, Magnetic Resonance Imaging (MRI) scanning has been the most rapidly developing field. Concerning the tumor’s size and specifics, diagnosing and classifying brain tumor’s is challenging and time-consuming for radiologists. The growth of abnormal cells in the brain is referred to as a brain tumor. A brain tumor is diagnosed in about 11,700 patients every year. It is estimated that 34% of males and 36% of females will survive five years after being diagnosed with malignant brain or different tumors. This study focuses on meningioma, pituitary, glioma and no tumors’, among the many brain tumors. Deep learning algorithms and machine learning methods were used to create an autonomous classification and segmentation system for brain tumors, significantly improving early detection. Using the Visual Geometry Group (16), parameters were set for training the model that detects brain tumors based on analysis of proposed literature solutions. Simple Convolution Neural Network (CNN) models such as VGG-16 and Efficient NetB7 perform well because they are among the highest-performing models. As a result of the study, quick, efficient, and precise decisions can be made using MRI to detect brain tumors. For this 7022 brain magnetic resonance images were used to train and test this model. According to experimental findings, the suggested differential deep CNN model could accurately categories MRI pictures of brain tumors, including aberrant and standard images, with a 98.19% accuracy rate.

Diabetic retinopathy grading based on multi-scale residual network and cross-attention module

An essential noninvasive medical diagnostic technique is magnetic resonance imaging (MRI), which is particularly useful for identifying brain cancers. While earlier algorithms proved effective on smaller MRI datasets, their performance suffered on bigger datasets. This study addresses the need for a swift and reliable brain tumor classification system capable of sustaining optimal performance across comprehensive MRI datasets. The convolutional neural network is implemented using the Keras library, incorporating the ResNet50 architecture as a pre-trained model. The ResNet50 model is fine-tuned for the specific brain tumor classification task, with a Global Average Pooling layer, dropout, and a final dense layer with softmax activation. Data augmentation techniques are employed to enhance the model’s robustness, including rotation, width and height shifts, and horizontal flips. The training process involves optimizing the model using the Adam optimizer with a learning rate of 0.0001. Early stopping, learning rate reduction on plateau, and model checkpointing are implemented as callbacks to ensure efficient training and prevent overfitting. The proposed model achieves a remarkable accuracy of 99.28 percent after 15 epochs. The classification task involves distinguishing among four classes: glioma, meningioma, pituitary, and no tumor.

Accurate classification of brain tumors is a major challenge in neuro-oncology, as the heterogeneity of tumor morphology and the overlap of radiological features limit the effectiveness of conventional diagnostic approaches. Early and reliable tumor characterization is essential for treatment planning, prognosis, and improved patient outcomes. Recent advances in artificial intelligence (AI) have enabled the development of deep learning frameworks that can augment radiological interpretation and support clinical decision-making. This study proposes and validates a hybrid computational framework that integrates convolutional neural networks (CNNs) with graph convolutional networks (GCNs) for automated classification of brain tumors from magnetic resonance imaging (MRI). A publicly available Kaggle-based MRI dataset was utilized, consisting of four categories: glioma, meningioma, pituitary tumor, and no-tumor. The proposed pipeline incorporated systematic preprocessing, transfer learning via the InceptionV3 architecture for hierarchical feature extraction, graph construction to model inter-feature relationships, and GCN-based relational learning for final classification. Hyperparameter optimization was performed using Particle Swarm Optimization (PSO) to improve generalizability. The experimental evaluation achieved an overall classification accuracy of 92.91%. Class-specific performance analysis demonstrated particularly high diagnostic accuracy in the no-tumor group (F1-score: 0.9963) and pituitary tumor group (F1-score: 0.9599). The incorporation of PSO tuning further improved the validation accuracy to 94.23%. The hybrid CNN-GCN framework exhibited robustness against imaging artifacts and irregular tumor boundaries, conditions that commonly challenge conventional classification techniques. The integration of CNN-based hierarchical feature extraction with GCN-based relational reasoning provides a significant advancement in automated brain tumor classification. This reproducible and intelligent diagnostic pipeline demonstrates strong potential for clinical translation by enhancing diagnostic precision, reducing radiologist workload, and facilitating timely therapeutic interventions. The findings support the integration of graph-based deep learning systems into smart healthcare ecosystems, where AI-assisted diagnostic tools can contribute to improved outcomes in neuro-oncology.

The classical neural network has provided remarkable results to diagnose neurological disorders against neuroimaging data. However, in terms of efficient and accurate classification, some standpoints need to be improved by utilizing high-speed computing tools. By integrating quantum computing phenomena with deep neural network approaches, this study proposes an AlexNet–quantum transfer learning method to diagnose neurodegenerative diseases using magnetic resonance imaging (MRI) dataset. The hybrid model is constructed by extracting an informative feature vector from high-dimensional data using a classical pre-trained AlexNet model and further feeding this network to a quantum variational circuit (QVC). Quantum circuit leverages quantum computing phenomena, quantum bits, and different quantum gates such as Hadamard and CNOT gate for transformation. The classical pre-trained model extracts the 4096 features from the MRI dataset by using AlexNet architecture and gives this vector as input to the quantum circuit. QVC generates a 4-dimensional vector and to transform this vector into a 2-dimensional vector, a fully connected layer is connected at the end to perform the binary classification task for a brain disorder. Furthermore, the classical–quantum model employs the quantum depth of six layers on pennyLane quantum simulators, presenting the classification accuracy of 97% for Parkinson’s disease (PD) and 96% for Alzheimer’s disease (AD) for 25 epochs. Besides this, pre-trained classical neural models are implemented for the classification of disorder and then, we compare the performance of the classical transfer learning model and hybrid classical–quantum transfer learning model. This comparison shows that the AlexNet–quantum learning model achieves beneficial results for classifying PD and AD. So, this work leverages the high-speed computational power using deep network learning and quantum circuit learning to offer insight into the practical application of quantum computers that speed up the performance of the model on real-world data in the healthcare domain.

Medical diagnostics require brain tumor classification to operate in real-time so the task demands accurate results with efficientprocessing abilities. A new hybrid deep learning solution merges convolutional neural networks (CNNs) with support vector machines(SVMs) to improve classification results as this paper describes. A total of four tumor categories including glioma, meningioma, andpituitary tumors together with no tumor appearance contribute to the magnetic resonance imaging (MRI) dataset are used for analysis. We applied and organized three pre-trained deep learning models: Alex-Net, DarkNet-19, and ResNet-50 for comparison. Anewly engineered compact CNN model linked with an SVM classifier brought decreased model dimensions while keeping excellent accuracy rates. Aproposed compact CNN model delivers 97.50% accuracy through its smaller 2.38 MB size and an additional SVM integration results in 97.45% accuracy using 1.43 MB. A Graphical User Interface (GUI) system comprising automated tumor classification capabilities is created to improve real-time systems that visualize MRI scans and illustrate predicted labels in addition to displaying confidence scores. A GUI enables smooth access to the trained model while being suitable for medical practice mobile healthcare environments and edge computing needs. The proposed system shows that lightweight architectures work excellently in real-time system applications especially when used for edge computing and mobile healthcare frameworks. The proposed solution demonstrates superiority over established models through its ability to scale efficiently.

Enhanced performance of Dark-Nets for brain tumor classification and segmentation using colormap-based superpixel techniques

Brain tumor is a severe cancer and a life-threatening disease. Thus, early detection is crucial in the process of treatment. Recent progress in the field of deep learning has contributed enormously to the health industry medical diagnosis. Convolutional neural networks (CNNs) have been intensively used as a deep learning approach to detect brain tumors using MRI images. Due to the limited dataset, deep learning algorithms and CNNs should be improved to be more efficient. Thus, one of the most known techniques used to improve model performance is Data Augmentation. This paper presents a detailed review of various CNN architectures and highlights the characteristics of particular models such as ResNet, AlexNet, and VGG. After that, we provide an efficient method for detecting brain tumors using magnetic resonance imaging (MRI) datasets based on CNN and data augmentation. Evaluation metrics values of the proposed solution prove that it succeeded in being a contribution to previous studies in terms of both deep architectural design and high detection success.

Brain tumor implies development of strange cells in brain. In cutting edge stages, brain tumor is most risky infection which can't be relieved. Thus, it ought to be identified in the beginning phases with the assistance of MRI (Magnetic Resonance Image). So, there will be more changes to the patient to endure. Quite possibly, the most functional and significant technique is to utilize Deep Neural Network (DNN). In this paper, a Deep Convolutional Neural Network (DCNN) based on VGG16 model has been developed to identify a tumor through brain Magnetic Resonance Imaging (MRI) dataset. In the clinical field, the strategies of machine learning (ML) and data mining hold a critical stand. It is effectively used to achieve the efficiency and exact location of tumor. The proposed technique involves automatic segmentation method based on Deep Convolution neural network (DCNN). It is layer based segmentation and classification technique. Different levels are engaged with the proposed technique, first step is data collection and then pre-processing & average filtering is done, after that segmentation, feature extraction and classification via DCNN is performed. In this paper, a fresh technique for classification of brain MRI images has been proposed using DCNN based softmax Classifier. The proposed system has been compared with the existing ones. The results are really encouraging as the loss is reduced and accuracy is increased. The proposed system achieved the training accuracy of 98.9% and validation accuracy of 100%. The training loss is reduced up to 0.0230 and the validation loss reduced up to 0.0109.

Identification of brain tumors and accurate grading at an early stage are crucial in cancer diagnosis, as a timely diagnosis can increase the chances of survival. Considering the challenges and risks of tumor biopsies, noninvasive imaging procedures such as Magnetic Resonance Imaging (MRI) are extensively used in analyzing brain tumors. Recent advances in the field of medical imaging with deep learning using three dimensional (3D) MRI is aiding the clinical experts significantly in the diagnosis of brain tumor. In this study, three BraTS MRI datasets named BraTS 2018, BraTS 2019 and BraTS 2020 are employed to classify brain tumor into high-grade glioma (HGG) and low-grade glioma (LGG) where each of the datasets contains four different sequences of 3D MRI brain images named T1-weighted MRI (T1), T1-weighted MRI with contrast enhancement (T1ce), T2-weighted MRI (T2), and Fluid Attenuated Inversion Recovery (FLAIR) for a single patient. This research is composed of two approaches where, in the first part, we propose a hybrid deep learning model named TimeDistributed-CNN-LSTM (TD-CNN-LSTM) combining 3D Convolutional Neural Network (CNN) and Long Short Term Memory (LSTM) where each layer of the architecture is wrapped with a TimeDistributed function. The objective of developing the hybrid model is to consider all the four 3D MRI sequences of each patient as a single input data because every sequence contains necessary information on tumor that can have an impact on improving cancer detection performance. However, interpreting all the MRI sequences together with optimal performance especially in 3D is quite challenging. Therefore, the model is developed with optimal configuration based on highest accuracy performing ablation study for layer architecture and hyper-parameters. In the second part, a 3D CNN model is trained respectively with each of the MRI sequences to compare the performance with TD-CNN-LSTM model. In this regard, for both of the models, BraTS 2018 and BraTS 2019 are combined to increase the number of images and used as train dataset where BraTS 2020 dataset is employed as the test dataset. Moreover, before training the models the datasets is preprocessed to ensure the highest performance. Our results of these two approaches demonstrate that the TD-CNN-LSTM network outperforms 3D CNN achieving the highest test accuracy of 98.90%. Later, to evaluate the performance consistency, the TD-CNN-LSTM model is evaluated with K-fold cross validation using different k values. The approach of putting together all the MRI sequences at a time using hybrid CNN-LSTM approach with good generalization capability to classify brain tumor can be used effectively in future Computer Aided Diagnosis (CAD) based research which can aid radiologists in medical diagnostics.

At present, in the field of video-based human action recognition, deep neural networks are mainly divided into two branches: the 2D convolutional neural network (CNN) and 3D CNN. However, 2D CNN’s temporal and spatial feature extraction processes are independent of each other, which means that it is easy to ignore the internal connection, affecting the performance of recognition. Although 3D CNN can extract the temporal and spatial features of the video sequence at the same time, the parameters of the 3D model increase exponentially, resulting in the model being difficult to train and transfer. To solve this problem, this article is based on 3D CNN combined with a residual structure and attention mechanism to improve the existing 3D CNN model, and we propose two types of human action recognition models (the Residual 3D Network (R3D) and Attention Residual 3D Network (AR3D)). Firstly, in this article, we propose a shallow feature extraction module and improve the ordinary 3D residual structure, which reduces the parameters and strengthens the extraction of temporal features. Secondly, we explore the application of the attention mechanism in human action recognition and design a 3D spatio-temporal attention mechanism module to strengthen the extraction of global features of human action. Finally, in order to make full use of the residual structure and attention mechanism, an Attention Residual 3D Network (AR3D) is proposed, and its two fusion strategies and corresponding model structure (AR3D_V1, AR3D_V2) are introduced in detail. Experiments show that the fused structure shows different degrees of performance improvement compared to a single structure.