Brain tumor analysis is essential to the timely diagnosis and effective treatment of patients. Tumor analysis is challenging because of tumor morphology factors like size, location, texture, and heteromorphic appearance in medical images. In this regard, a novel two-phase deep learning-based framework is proposed to detect and categorize brain tumors in magnetic resonance images (MRIs). In the first phase, a novel deep-boosted features space and ensemble classifiers (DBFS-EC) scheme is proposed to effectively detect tumor MRI images from healthy individuals. The deep-boosted feature space is achieved through customized and well-performing deep convolutional neural networks (CNNs), and consequently, fed into the ensemble of machine learning (ML) classifiers. While in the second phase, a new hybrid features fusion-based brain-tumor classification approach is proposed, comprised of both static and dynamic features with an ML classifier to categorize different tumor types. The dynamic features are extracted from the proposed brain region-edge net (BRAIN-RENet) CNN, which is able to learn the heteromorphic and inconsistent behavior of various tumors. In contrast, the static features are extracted by using a histogram of gradients (HOG) feature descriptor. The effectiveness of the proposed two-phase brain tumor analysis framework is validated on two standard benchmark datasets, which were collected from Kaggle and Figshare and contain different types of tumors, including glioma, meningioma, pituitary, and normal images. Experimental results suggest that the proposed DBFS-EC detection scheme outperforms the standard and achieved accuracy (99.56%), precision (0.9991), recall (0.9899), F1-Score (0.9945), MCC (0.9892), and AUC-PR (0.9990). The classification scheme, based on the fusion of feature spaces of proposed BRAIN-RENet and HOG, outperform state-of-the-art methods significantly in terms of recall (0.9913), precision (0.9906), accuracy (99.20%), and F1-Score (0.9909) in the CE-MRI dataset.

Brain tumor classification is essential for clinical diagnosis and treatment planning. Deep learning models have shown great promise in this task, but they are often challenged by the complex and diverse nature of brain tumors. To address this challenge, we propose a novel deep residual and region-based convolutional neural network (CNN) architecture, called Res-BRNet, for brain tumor classification using magnetic resonance imaging (MRI) scans. Res-BRNet employs a systematic combination of regional and boundary-based operations within modified spatial and residual blocks. The spatial blocks extract homogeneity, heterogeneity, and boundary-related features of brain tumors, while the residual blocks significantly capture local and global texture variations. We evaluated the performance of Res-BRNet on a challenging dataset collected from Kaggle repositories, Br35H, and figshare, containing various tumor categories, including meningioma, glioma, pituitary, and healthy images. Res-BRNet outperformed standard CNN models, achieving excellent accuracy (98.22%), sensitivity (0.9811), F1-score (0.9841), and precision (0.9822). Our results suggest that Res-BRNet is a promising tool for brain tumor classification, with the potential to improve the accuracy and efficiency of clinical diagnosis and treatment planning.

Brain tumor detection, at the early stages of its development for the timely cure of the patient, is a challenging task due to its complex nonlinear nature. We propose a dual-channel brain tumor detection (DC-BTD) framework for magnetic resonance imaging scans with optimum false negatives, based on the idea of using D-channel for extremely discriminant dynamic features and S-channel for static features using data normalization, augmentation, and different machine learning (ML) classifiers, namely, support vector machine, k-nearest neighbor, naïve Bayes, and XGBoost. The D-channel features are extracted using a proposed Fine-Tuned Convolutional Neural Network (FT-CNN), while the S-channel features are extracted using the histogram of oriented gradients (HOG)- and local binary patterns (LBP)-based operators. The dual-channel data form the hybrid feature space (HFS) gives improved performance using two types of features. Computer experiments have been conducted on publically available brain tumors dataset obtained from Nanfang Hospital, Guangzhou, and Tianjin Medical University General Hospital, China. The finding of the current study shows that the proposed framework for brain-tumor prediction outperforms other contemporary existing methods with the highest generalization performance as 98.70% (accuracy) and 98.56% (F_score) on the same dataset that can lead to better brain tumor detection at an early stage. Highlights

Early detection of abnormalities in chest X-rays is essential for COVID-19 diagnosis and analysis. It can be effective for controlling pandemic spread by contact tracing, as well as for effective treatment of COVID-19 infection. In the proposed work, we presented a deep hybrid learning-based framework for the detection of COVID-19 using chest X-ray images. We developed a novel computationally light and optimized deep Convolutional Neural Networks (CNNs) based framework for chest X-ray analysis. We proposed a new COV-Net to learn COVID-specific patterns from chest X-rays and employed several machine learning classifiers to enhance the discrimination power of the presented framework. Systematic exploitation of max-pooling operations facilitates the proposed COV-Net in learning the boundaries of infected patterns in chest X-rays and helps for multi-class classification of two diverse infection types along with normal images. The proposed framework has been evaluated on a publicly available benchmark dataset containing X-ray images of coronavirus-infected, pneumonia-infected, and normal patients. The empirical performance of the proposed method with developed COV-Net and support vector machine is compared with the state-of-the-art deep models which show that the proposed deep hybrid learning-based method achieves 96.69% recall, 96.72% precision, 96.73% accuracy, and 96.71% F-score. For multi-class classification and binary classification of COVID-19 and pneumonia, the proposed model achieved 99.21% recall, 99.22% precision, 99.21% F-score, and 99.23% accuracy.

Brain tumors are among the most life-threatening diseases, where early detection and accurate classification are critical for effective diagnosis and treatment planning. Although deep learning-based computer-aided diagnosis systems have shown remarkable progress, conventional convolutional neural networks (CNNs) and Vision Transformers (ViTs) still face challenges such as limited global-local feature integration, sensitivity to structural heterogeneity, and high computational cost. To address these limitations, this study proposes a novel hybrid framework, termed CE-RS-SBCIT, which integrates residual and spatial learning with transformer-driven contextual modeling. The proposed framework architecture combines five core components: (i) a smoothing and boundary-based CNN-integrated Transformer (SBCIT) for enhanced local-global representation learning, (ii) residual and spatial CNNs modules for capturing fine-grained textures and structural variations, (iii) a multi-scale channel attention and squeezing (MSCAS) mechanism for adaptive feature weighting, (iv) a channel enhancement (CE) strategy for enriched feature fusion, and (v) a pixel-level spatial attention module for improved inter-class discrimination. Extensive experiments on publicly available magnetic resonance imaging (MRI) datasets demonstrate that the proposed framework achieves superior performance, with 98.30% accuracy, 98.08% sensitivity, 98.43% precision, and an F1 Score of 98.25%. Comparative and ablation analyses further confirm the effectiveness of the proposed architecture in capturing both heterogeneous and homogeneous tumor characteristics while maintaining computational efficiency. These results indicate that CE-RS-SBCIT provides a robust and generalizable solution for brain tumor classification and has strong potential for deployment in clinical decision support systems.

Brain tumors analysis is important in timely diagnosis and effective treatment to cure patients. Tumor analysis is challenging because of tumor morphology like size, location, texture, and heteromorphic appearance in the medical images. In this regard, a novel two-phase deep learning-based framework is proposed to detect and categorize brain tumors in magnetic resonance images (MRIs). In the first phase, a novel deep boosted features and ensemble classifiers (DBF-EC) scheme is proposed to detect tumor MRI images from healthy individuals effectively. The deep boosted feature space is achieved through the customized and well-performing deep convolutional neural networks (CNNs), and consequently, fed into the ensemble of machine learning (ML) classifiers. While in the second phase, a new hybrid features fusion-based brain tumor classification approach is proposed, comprised of dynamic-static feature and ML classifier to categorize different tumor types. The dynamic features are extracted from the proposed BRAIN-RENet CNN, which carefully learns heteromorphic and inconsistent behavior of various tumors, while the static features are extracted using HOG. The effectiveness of the proposed two-phase brain tumor analysis framework is validated on two standard benchmark datasets; collected from Kaggle and Figshare containing different types of tumor, including glioma, meningioma, pituitary, and normal images. Experimental results proved that the proposed DBF-EC detection scheme outperforms and achieved accuracy (99.56%), precision (0.9991), recall (0.9899), F1-Score (0.9945), MCC (0.9892), and AUC-PR (0.9990). While the classification scheme, the joint employment of the deep features fusion of proposed BRAIN-RENet and HOG features improves performance significantly in terms of recall (0.9913), precision (0.9906), F1-Score (0.9909), and accuracy (99.20%) on diverse datasets.