A Hybrid Deep Learning-Based Approach for Brain Tumor Classification

Brain tumor remain one of the most life-threatening forms of cancer, and early and accurate diagnosis is crucialfor effective treatment planning and improving patient outcomes. Magnetic Resonance Imaging (MRI) serves as a primary modality for brain tumor detection; however, manual interpretation of these scans is often time-consuming and subject to inter-observer variability. Recent advances in Machine Learning (ML) and Deep Learning (DL) offer promising tools to automate and enhance tumor detection and segmentation in medical images. This research paper presents a comprehensive study on the application of ML and DL techniques for brain tumor detection, focusing on both classification and segmentation tasks. Various algorithms, including traditional ML classifiers and state-of- the-art Convolutional Neural Networks (CNNs), were evaluated on publicly available datasets. The proposed deep learning models demonstrated superior performance in identifying tumor regions with high accuracy and robustness. Furthermore, the paper discusses the challenges associated with data preprocessing, model interpretability, and real- time deployment, particularly in the context of medical science. The results underscore the potential of integrating ML/DL-based systems into clinical workflows to support radiologists and enhance diagnostic efficiency.

Artificial intelligence (AI)-based approaches are crucial in computer-aided diagnosis (CAD) for various medical applications. Their ability to quickly and accurately learn from complex data is remarkable. Deep learning (DL) models have shown promising results in accurately classifying Alzheimer's disease (AD) and its related cognitive states, Early Mild Cognitive Impairment (EMCI) and Late Mild Cognitive Impairment (LMCI), along with the healthy conditions known as Cognitively Normal (CN). This offers valuable insights into disease progression and diagnosis. However, certain traditional machine learning (ML) classifiers perform equally well or even better than DL models, requiring less training data. This is particularly valuable in CAD in situations with limited labeled datasets. In this paper, we propose an ensemble classifier based on ML models for magnetic resonance imaging (MRI) data, which achieved an impressive accuracy of 96.52%. This represents a 3-5% improvement over the best individual classifier. We evaluated popular ML classifiers for AD classification under both data-scarce and data-rich conditions using the Alzheimer's Disease Neuroimaging Initiative and Open Access Series of Imaging Studies datasets. By comparing the results to state-of-the-art CNN-centric DL algorithms, we gain insights into the strengths and weaknesses of each approach. This work will help users to select the most suitable algorithm for AD classification based on data availability.

Deep learning’s swift development has generated substantial excitement about its application in medical imaging. Machine learning (ML) methods can support radiologists in diagnosing breast cancer (BC) without resorting to invasive procedures. However, traditional ML classifiers require the extraction of detailed hand-crafted features, which is a time-intensive task to achieve accurate results. Hence, this paper proposes a novel Feature-driven Breast Cancer Classification using the Modified Loss and Activation function-assisted LeNet (MLAL) model, named F-BCC-ML. The process of detecting BC using mammogram images comprises several key stages. In the first step, the image undergoes enhancement using the Improved Bilateral Filtering Technique (IBFT), which reduces the noise while conserving critical structural details like edges. Next, the image is subjected to segmentation using SegNet, a deep-learning model designed for semantic segmentation. After segmentation, the next phase is feature extraction, where various features like Weber Local descriptor assisted Local Gabor XOR Pattern (WLD-LGXP) for texture analysis, Median Binary Pattern (MBP), colour features, and deep features are derived from the segmented image. Once the features are extracted, they are fed into the classification stage, where the Modified Loss and Activation function assisted LeNet (MLAL) model, more sophisticated Deep Convolutional Neural Network (DCNN) are used to classify the image as either normal or cancerous. The result is a prediction that indicates whether the breast tissue is benign or shows signs of cancer, helping radiologists make more accurate and informed decisions. The MLAL+DCNN accomplished the maximum accuracy of 0.936, precision of 0.947 and F-measure of 0.942, respectively.

Early diagnosis and treatment is very essential in monitoring Brain tumor using MRI images. Convolutional Neural Networks (CNN) and Machine Learning (ML) classifiers have been widely used but there is not much work on how feature selection techniques would affect the performance of the CNN. Secondly, there is a need for investigation concerning small dataset adaptability and ML-CNN comparisons. To improve the classification accuracy, we integrate Univariate, Recursive Feature Elimination (RFE), Recursive Feature Elimination with Cross Validation (RFECV) with CNN in this study. Preprocessing, feature extraction & selection was carried out on the dataset consisting of 253 MRI images and they are classified using CNN and ML models (Logistic Regression, Decision Tree, Random Forest, Naïve Bayes). With the results 96%, CNN with Univariate Feature Selection performed better than ML classifiers, and other selection techniques. The results demonstrate that feature selection is necessary to get the best performance out of CNN models operating on small datasets. Future studies should be based on different deep learning architectures to improve classification and application in other datasets.

A brain tumor is a mass or growth of abnormal cells in our brain. Many different types of brain tumors exist. Some brain tumors are noncancerous (benign), and some brain tumors are cancerous (malignant). Brain tumors can begin in your brain (primary brain tumors), or cancer can begin in other parts of your body and spread to your brain (secondary, or metastatic, brain tumors). Brain tumor treatment options depend on the type of brain tumor you have, as well as its size and location. The classification of brain tumors is performed by biopsy, which is not usually conducted before definitive brain surgery. The improvement of technology and machine learning can help radiologists in tumor diagnostics without invasive measures. A machine-learning algorithm that has achieved substantial results in image classification is the convolution neural network (CNN). It is predicted that the success of the obtained results will increase if the CNN method is supported by adding extra feature extraction methods and classify successfully brain tumor normal and abnormal image.

One of the most dangerous diseases is a brain tumor that may affect children as well as adults both. Brain tumors are responsible for 80-90 percent of all primary Central Nervous System (CNS) cancers. Each year, around 12,000 A brain tumor is discovered in a person. When diagnosed with a malignant brain or CNS tumor, males and the five-year survival rate for women is approximately 33% and 37% respectively. There are several types of brain tumors, including pituitary tumors, malignant tumors, benign tumors and more. Increased life expectancy for patients should be achieved by appropriate therapy, planning, and precise diagnostics. Magnetic resonance imaging is the most effective method for identifying brain tumors (MRI). Scanners produce massive volumes of picture data. The radiologist examines these pictures. Automated classification technologies such as Artificial intelligence (AI) and machine learning (ML) have regularly outperformed manual categorization in terms of accuracy. As a consequence, offering a system that employs Deep Learning Techniques and Algorithms such as ANN (Artificial Neural Networks), CNN (Convolution Neural Networks), TL (Transfer Learning) and GLCM for recognizing and tracking it will benefit doctors worldwide.

Globally, in 2016, 1 out of 11 adults suffered from diabetes mellitus. Diabetic foot ulcers (DFU) are a major complication of this disease, which if not managed properly can lead to amputation. Current clinical approaches to DFU treatment rely on patient and clinician vigilance, which has significant limitations, such as the high cost involved in the diagnosis, treatment, and lengthy care of the DFU. We collected an extensive dataset of foot images, which contain DFU from different patients. In this DFU classification problem, we assessed the two classes as normal skin (healthy skin) and abnormal skin (DFU). In this paper, we have proposed the use of machine learning algorithms to extract the features for DFU and healthy skin patches to understand the differences in the computer vision perspective. This experiment is performed to evaluate the skin conditions of both classes that are at high risk of misclassification by computer vision algorithms. Furthermore, we used convolutional neural networks for the first time in this binary classification. We have proposed a novel convolutional neural network architecture, DFUNet, with better feature extraction to identify the feature differences between healthy skin and the DFU. Using 10-fold cross validation, DFUNet achieved an AUC score of 0.961. This outperformed both the traditional machine learning and deep learning classifiers we have tested. Here, we present the development of a novel and highly sensitive DFUNet for objectively detecting the presence of DFUs. This novel approach has the potential to deliver a paradigm shift in diabetic foot care among diabetic patients, which represent a cost-effective, remote, and convenient healthcare solution.

Accurate detection and classification of brain tumors are essential for timely diagnosis and effective treatment planning. This study presents an integrated framework leveraging both machine learning (ML) and deep learning (DL) models for brain tumor detection and classification using MRI images. Two publicly available datasets are utilized: one for binary classification (tumor vs. no tumor) and another for multiclass classification (glioma, meningioma, and pituitary tumors). Comprehensive preprocessing steps, including resizing, feature extraction using the Gray Level Co-occurrence Matrix (GLCM), and feature selection via Chi-square testing, were employed to optimize the dataset for modeling. Machine learning models such as Decision Trees, K-Nearest Neighbors (KNN), Support Vector Machines (SVM), and AdaBoost were compared with deep learning architectures like Convolutional Neural Networks (CNNs) and the pre-trained VGG16 model. Hyperparameter optimization techniques, including grid search and the Adam optimizer, were used to enhance model performance. The models were evaluated using metrics such as accuracy, precision, recall, F1-score, Mean Squared Error (MSE), and Mean Absolute Error (MAE). Results indicate that the VGG16 model consistently outperformed other approaches, achieving high validation accuracy. This study highlights the potential of integrating ML and DL techniques for accurate and efficient brain tumor detection and classification, offering valuable tools for medical diagnostics.

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

Mobile fitness applications provide the opportunity to show users real-time feedback on their current fitness activity. For such applications, it is essential to accurately track the user's current fitness activity using available mobile sensors, such as inertial measurement units (IMUs). Convolutional neural networks (CNNs) have been shown to produce strong results in different time series classification tasks, including the recognition of daily living activities. However, fitness activities can present unique challenges to the human activity recognition task (HAR), including greater similarity between individual activities and fewer available data for model training. In this paper, we evaluate the applicability of CNNs to the fitness activity recognition task (FAR) using IMU data and determine the impact of input data size and sensor count on performance. For this purpose, we adapted three existing CNN architectures to the FAR task and designed a fourth CNN variant, which we call the scaling fully convolutional network (Scaling-FCN). We designed a preprocessing pipeline and recorded a running exercise data set with 20 participants, in which we evaluated the respective recognition performances of the four networks, comparing them with three traditional machine learning (ML) methods commonly used in HAR. Although CNN architectures achieve at least 94% test accuracy in all scenarios, two traditional ML architectures surpass them in the default scenario, with support vector machines (SVMs) achieving 99.00 ± 0.34% test accuracy. The removal of all sensors except one foot sensor reduced the performance of traditional ML architectures but improved the performance of CNN architectures on our data set, with our Scaling-FCN reaching the highest accuracy of 99.86 ± 0.11% on the test set. Our results suggest that CNNs are generally well suited for fitness activity recognition, and noticeable performance improvements can be achieved if sensors are dropped selectively, although traditional ML architectures can still compete with or even surpass CNNs when favorable input data are utilized.

The global food security and agricultural sustainability face a major threat from plant diseases. Effective management requires both early and accurate identification of these diseases. The development of Artificial Intelligence (AI) has led Machine Learning (ML) techniques to become promising tools for automated disease detection and classification. The paper presents an extensive evaluation of current progress in employing ML methods for plant disease detection. The role of image processing combined with deep learning and traditional ML classifiers in disease identification is discussed in detail. A detailed literature survey is provided followed by a method- dataset-performance metrics comparison. The paper also discusses future research directions and challenges.

IntroductionAccurate classification of brain tumors from MRI scans is a critical task for improving patient outcomes. Machine learning (ML) and deep learning (DL) methods have shown promise in this domain, but their relative performance remains unclear.MethodsThis study evaluates several ML and DL techniques using the BraTS 2024 dataset. The models assessed include traditional algorithms such as Random Forest and advanced deep learning architectures including Simple CNN, VGG16, VGG19, ResNet50, Inception-ResNetV2, and EfficientNet. Preprocessing strategies were applied to optimize model performance.ResultsThe Random Forest classifier achieved the highest accuracy of 87%, outperforming all deep learning models, which achieved accuracy in the range of 47% to 70%. This indicates that traditional ML approaches can sometimes surpass state-of-the-art DL methods in tumor classification tasks.DiscussionThe findings highlight the importance of model selection and parameter tuning in automated brain tumor diagnosis. While deep learning models are generally considered standard for image analysis, Random Forest demonstrated superior performance in this context. This underscores the need for fine-grained consideration of dataset characteristics, computational resources, and diagnostic requirements.ConclusionThe study shows that carefully selected and optimized ML approaches can improve tumor classification and support more accurate and efficient diagnostic systems for brain tumor patients.

In today’s digital world, Android phones play a vital part in a variety of facets of both professionals and individuals’ personal and professional lives. Android phones are great for getting things done faster and more organized. The proportionate increase in the number of malicious applications has also been seen to be expanding. Since the play store offers millions of apps, detection of malware apps is challenging task. In this paper, a methodology is introduced for detecting malware in Android applications through the utilization of global image shape transform (GIST) features extracted from grayscale images of the applications. The dataset comprises samples of both malware and benign apps collected from the virus share website. After converting the apps into grayscale images, GIST features are extracted to capture their global spatial layout. Various machine learning (ML) algorithms, such as logistic regression (LR), k-nearest neighbour (KNN), AdaBoost, decision tree (DT), Naïve Bayes (NB), random forest (RF), support vector machine (SVM), extra tree classifier (ETC), and gradient boosting (GB), are employed to classify the applications according to their GIST features. Furthermore, a feed forward neural network (FFNN) is utilized as a deep learning (DL) technique to further improve the accuracy of classification. The performance of each algorithm is evaluated using metrics such as accuracy, precision and recall. The results demonstrated that the FFNN achieves superior accuracy compared to traditional ML classifiers, indicating its effectiveness in detecting malware in Android apps.

Early and accurate identification of brain tumors is essential for determining effective treatment strategies and improving patient outcomes. Artificial intelligence (AI) and deep learning (DL) techniques have shown promise in automating diagnostic tasks based on magnetic resonance imaging (MRI). This study evaluates the performance of four pre-trained deep convolutional neural network (CNN) architectures for the automatic multi-class classification of brain tumors into four categories: Glioma, Meningioma, Pituitary, and No Tumor. The proposed approach utilizes the publicly accessible Brain Tumor MRI Msoud dataset, consisting of 7023 images, with 5712 provided for training and 1311 for testing. To assess the impact of data availability, subsets containing 25%, 50%, 75%, and 100% of the training data were used. A stratified five-fold cross-validation technique was applied. The CNN architectures evaluated include DeiT3_base_patch16_224, Xception41, Inception_v4, and Swin_Tiny_Patch4_Window7_224, all fine-tuned using transfer learning. The training pipeline incorporated advanced preprocessing and image data augmentation techniques to enhance robustness and mitigate overfitting. Among the models tested, Swin_Tiny_Patch4_Window7_224 achieved the highest classification Accuracy of 99.24% on the test set using 75% of the training data. This model demonstrated superior generalization across all tumor classes and effectively addressed class imbalance issues. Furthermore, we deployed and benchmarked the best-performing DL model on embedded AI platforms (Jetson AGX Xavier and Orin Nano), demonstrating their capability for real-time inference and highlighting their feasibility for edge-based clinical deployment. The results highlight the strong potential of pre-trained deep CNN and transformer-based architectures in medical image analysis. The proposed approach provides a scalable and energy-efficient solution for automated brain tumor diagnosis, facilitating the integration of AI into clinical workflows.

Brain tumour segmentation can improve diagnostics efficiency, rise the prediction rate and treatment planning. This will help the doctors and experts in their work. Where many types of brain tumour may be classified easily, the gliomas tumour is challenging to be segmented because of the diffusion between the tumour and the surrounding edema. Another important challenge with this type of brain tumour is that the tumour may grow anywhere in the brain with different shape and size. Brain cancer presents one of the most famous diseases over the world, which encourage the researchers to find a high-throughput system for tumour detection and classification. Several approaches have been proposed to design automatic detection and classification systems. This paper presents an integrated framework to segment the gliomas brain tumour automatically using pixel clustering for the MRI images foreground and background and classify its type based on deep learning mechanism, which is the convolutional neural network. In this work, a novel segmentation and classification system is proposed to detect the tumour cells and classify the brain image if it is healthy or not. After collecting data for healthy and non-healthy brain images, satisfactory results are found and registered using computer vision approaches. This approach can be used as a part of a bigger diagnosis system for breast tumour detection and manipulation.