Deep Learning for MRI Image Classification using CNN

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Deep Learning for MRI Image Classification using CNN Chandana T R1, H P Mohan Kumar2 1Master of Computer Application PES College of Engineering, Mandya, Karnataka, India

2Computer Science and Engineering, PES College of Engineering, Mandya, Karnataka, India

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Abstract - The sophisticated non-invasive diagnostic

beginning with image pre-processing using a Gaussian filter to eliminate noise. This is followed by image segmentation through the Fuzzy C-Means (FCM) algorithm and the extraction of statistical features such as kurtosis, mean, and median. The classification is then performed on MRI scans of various body parts including the face, head, skull, foot, and chest, using three machine learning techniques: Probabilistic Neural Network (PNN), (SVM), and k-Nearest Neighbor (KNN) algorithm. The dataset consists of 300 MRI images sourced from Apollo Hospitals, Hyderabad. Among the methods evaluated, SVM demonstrated superior performance with an overall 93.1%, outperforming the other approaches

technique known as magnetic resonance imaging, or MRI, makes it possible to see inside body parts with remarkable precision. Magnetic resonance imaging (MRI) makes it easier to create comprehensive pictures of organs, soft tissues, and the musculoskeletal system by fusing radio frequency radiation, strong magnetic fields, and computer processing.

MRI is a safer alternative to diagnostic procedures like CT scans and X-rays since it doesn't expose patients to ionizing radiation, especially when it comes to soft tissue evaluation.

Clinicians frequently rely on MRI to identify, assess, and track various medical conditions, as it provides highresolution images that clearly differentiate between healthy and abnormal tissues.

[2] Deep learning and machine learning have significantly

improved computer-aided detection systems by enhancing diagnostic accuracy in medical imaging. These techniques enable efficient analysis of large imaging datasets, particularly aiding in early detection of conditions like cancer and tumors

Key Words: Medical imaging, MRI Image, Deep Learning

1.INTRODUCTION MRI plays an essential role in contemporary medical diagnostics by offering high-resolution visualizations of the body’s internal anatomy. Despite its effectiveness, analyzing MRI scans manually can be both time-intensive and prone to inconsistencies by different radiologists. The emergence of deep learning—particularly CNNs—has introduced promising methods for automating with considerable precision. In order to organize the categorization of MRI data, this study looks into CNN structures, with the objective of enhancing the diagnostic workflow and delivering consistent, data-driven insights to support clinicians. The primary aim is to develop a robust, accurate, and timeefficient system that can help professionals in the interpretation of MRI scans, thereby contributing to more timely and accurate diagnoses. Employing deep learning strategies for MRI image classification signifies a major step forward in medical imaging technologies. By leveraging CNN models, the project aspires to minimize the diagnostic burden on healthcare providers, increase reliability in image assessment, and ultimately foster improved outcomes in patient care.

[3] The architecture combines features from paired DCNNs and feeds them into a synergic network to determine image category similarity. Synergic error signals, generated when one network misclassifies, help refine the learning process.

Trained end-to-end, the SDL approach achieves state-of-theart results on datasets like Image CLEF and ISIC .

[4 In addition, the review examines critical aspects of the

classification workflow, such as dataset curation, feature extraction, and performance evaluation. Emphasis is placed on the role of large, annotated datasets, as well as techniques like transfer learning and data augmentation, which contribute significantly to model accuracy and generalizability in medical imaging tasks.

[5] The paper "Deep learning-based image classification of MRI brain image" presents a convolutional neural network (CNN) approach for classifying brain MRI scans into normal and abnormal categories. It emphasizes automated feature extraction and high accuracy, reducing reliance on manual interpretation. The model demonstrates strong performance in early detection of brain disorders, supporting clinical decision-making.

2. LITERATURE SURVEY [1] Identifying fractured regions in MRI images begins with accurate image classification, which serves as a crucial initial step. This paper introduces an effective approach for classifying MRI scans to assist medical professionals and radiologists in making well-informed diagnostic decisions.

The proposed methodology involves a systematic process

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[6] The paper "MRI Brain Images Classification Using

Convolutional Neural Networks" explores the use of CNNs for accurately classifying brain MRI images into healthy and diseased categories. It highlights the strength of deep learning in automatic feature extraction and improved

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diagnostic precision. The study confirms that CNN-based models outperform traditional methods in medical image classification tasks.

data-driven learning, to improve an automated MRI image classification system to discourse these issues.

Convolutional Neural Networks (CNNs) are highly effective in classifying MRI pictures because of their capacity to automatically learn and extract layered properties from complex picture data. Particularly in MRI analysis, medical imaging, CNNs are capable of examining pixel intensity distributions and spatial arrangements to uncover subtle indicators of various tissue types or pathological conditions.

When trained on extensive, annotated MRI datasets, CNNs can accurately differentiate between healthy and diseased tissues, identify specific abnormalities, and support clinical decision-making. This automated approach minimizes the dependence on manual interpretation, thereby reducing inter-observer variability and expediting the diagnostic process. CNNs therefore play a major role in enhancing medical image analysis's precision and effectiveness.

[7] The network is trained under a multitask framework for tumor detection, classification, and localization, integrating advanced feature extraction and data augmentation techniques to improve accuracy.

[8] The study introduces a tailored convolutional neural network designed specifically for brain tumor detection from MRI scans, addressing the transparency issues typical of standard deep learning models.

[9] The model integrates the LIME explainability technique, which highlights image regions influencing the classification decisions, boosting transparency and clinician confidence.

[10] The study presents a fully automated multiscale CNN that processes MRI slices at three spatial resolutions (large, medium, small) via parallel pathways, mimicking human visual perception for improved feature extraction.

[11] The study highlights the utility of transfer learning for efficient model training and robust performance in brain tumor classification, while noting persistent challenges in improving recall for certain tumor types and the need for enhanced model interpretability.

[12] The study concludes that the proposed CNN strikes an optimal balance between accuracy, speed, and simplicity, making it a suitable choice for integration in clinical systems for early brain tumor.

1.

CNNs remove the time-consuming and error-prone human feature extraction by automatically extracting relevant characteristics from MRI images.

2.

When trained on suitably big and varied datasets, CNNs have shown great accuracy in medical picture classification tasks.

3.

Once trained, CNNs can process MRI scans quickly, potentially reducing turnaround times.

[13] A deep neural network with attention modules extracts multiscale features from segmented regions of interest (ROIs), feeding into an ensemble classifier to distinguish tumor types accurately.

[14] Their approach integrates segmentation, feature extraction, and ensemble fusion—leveraging complementary strengths of each network—to achieve highly accurate tumor detection.

Fig – 1: System Architecture

[15] It highlights that, across multiple disorders and MRI modalities, CNN-based methods consistently outperform other architectures such as RNNs, autoencoders, and DNNs.

It refers to the conceptual framework that outlines the structure, functionality, and operational aspects of a system.

It acts a foundational model that captures how various components of the application, function, and are organized.

An architectural description provides a structured and formal representation of this model, enabling clear analysis and reasoning about the system's design, behavior, and inter-component relationships.

3. Proposed Work Manual interpretation and classification of MRI scans present several challenges, such as being time-intensive, subject to variability among radiologists, and prone to human error. These issues can compromise diagnostic accuracy and potentially delay or mislead clinical decisions, thereby affecting the overall quality of patient care. This project suggests using convolutional neural networks (CNNs), which can increase diagnostic consistency, speed, and reliability through sophisticated pattern recognition and

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The provided system architecture shows how a algorithm CNN is used in an MRI image categorization system. The first step of the procedure is data collecting, which comprises acquiring MRI pictures from publically accessible sources.

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After that, these photos go through preprocessing, which involves reducing and normalizing them to preserve consistency and enhance model performance. To facilitate learning and assessment, training and testing datasets are further divide.

model to process the data consistently and effectively during training and inference.

Convolutional Layers: The first convolutional layer in the CNN applies 32 filters, each of size 3×3, to the input MRI images. This convolutional operation is essential for detecting low-level features, such as edges, corners, and textures, which are critical for the initial stages of image analysis. These features serve as the foundational elements that deeper layers can build upon to recognize more complex patterns.

With the trained dataset model is trained using cnn, with specific hyperparameters like learning rate and batch size.

After training, the model is evaluated to measure its accuracy and effectiveness. Finally, in the cataloging stage, the trained model is tested on unseen data to classify the MRI images into their respective output classes. This structured pipeline ensures accurate, efficient, and scalable image classification for medical application.

Following the convolution, the ReLU (Rectified Linear Unit) activation function is employed. ReLU introduces nonlinearity into the model by transforming all negative pixel values to zero while retaining positive values. This activation function enables the network to learn complex patterns and improves the model's ability to approximate non-linear relationships within the image data.

4. RESEARCH AND METHODOLOGY The usage of CNN algorithm for MRI image categorization follows a structured method. The approach begins with the gathering and processing of MRI datasets, which involves normalizing pixel intensities and scaling pictures to maintain uniformity throughout the dataset. The CNN architecture is then constructed to contain crucial components such as, recognizing edges and textures, pooling layers for dimensionality reduction while maintaining essential information, and fully connected layers for final classification.

Pooling Layers: First Max Pooling Layer: The output of the first convolutional layer is passed through a max-pooling layer with a 2x2 filter. This layer reduces the spatial dimensions of the feature maps by selecting the maximum value in each 2x2 window, which helps in reducing the complexity of the model and preventing overfitting.

Further

Convolution and Pooling: Second Convolutional Layer: Similar to the first, this layer applies another set of 32 convolutional filters of size 3x3 to the pooled feature maps, learning more complex features from the MRI images.

The intricacy of the classification problem and the processing resources at hand determine which CNN design is best. Data augmentation techniques like rotation, flipping, and zooming are used to artificially increase the training dataset and introduce variability in order to enhance the model's capacity for generalization.

Second Max Pooling Layer: Another max-pooling layer follows, further reducing the spatial dimensions of the feature maps.

After constructing the model architecture, training is performed on the augmented dataset, during which the CNN learns to alter its parameters via back propagation.

Following training, Performance metrics like accuracy and loss are used to assess the model on a different test set. This analysis sheds light on the model's ability to accurately identify MRI images and its potential to support medical diagnosis.

Flattening Layer: This layer flattens the 2D feature maps into a 1D vector. This flattened vector serves as the input for the fully connected layers.

Fully Connected Layers: The network can learn high-

4.1 CNN Algorithm

level representations of the characteristics by passing the flattened vector to the first fully connected layer, which has 256 neurons.

Input Layer: Since MRI images can vary in their original

Output Layer: If there are 8 possible risk classes, the

dimensions, they are typically resized to a standardized resolution, such as 224×224 pixels, to ensure uniformity across the dataset. This resolution defines the spatial size of each input image.

output layer will have 8 neurons with a Softmax activation function, providing a probability distribution over the classes.

MRI scans are generally grayscale, meaning they consist of a single channel. As a result, the input shape for these images is typically defined as 224×224×1, where "1" denotes the number of color channels. Standardizing the input dimensions is a crucial preprocessing step, as it enables the

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The proposed MRI image classification model demonstrates superior accuracy when compared to existing approaches discussed in related research[1]. Through optimized preprocessing, feature extraction, and the use of an effective machine learning algorithm (CNN), the model achieved an accuracy of 93.1%.

Fig – 3: MRI Precited Image Fig – 2 CNN Algorithm

6. CONCLUSION

Dataset: The collection contains grayscale MRI pictures,

For classifying MRI images a CNN Algorithm offers a compelling approach to addressing the limitations of manual image analysis. This research aims to create a highly accurate and effective automated classification model by utilizing a vast and varied dataset in conjunction with cutting-edge deep learning techniques.

methodically categorized into four unique categories: Brain, Chest, Lower Body (Kidney Pelvis), and Lumbar. Each picture depicts a distinct anatomical location, obtained using MRI images, and shows diverse tissue architectures and internal compositions vital for medical study.

This model is trained and validated using this heterogeneous dataset with the goal of automatically categorizing the photos into the appropriate classes. The model's capacity to learn discriminative characteristics across various body parts is improved by the addition of diverse anatomical areas, which eventually improves the model's generalization and diagnostic capabilities. The model may help medical personnel recognize and interpret MRI scans more accurately and efficiently if it can successfully understand these variances.

The successful deployment of such a system holds the potential to enhance diagnostic precision, alleviate the workload of medical professionals, and ultimately contribute to improved patient outcomes. Moreover, the techniques and results presented in this study highlight the growing role of CNN-based solutions in medical imaging, signaling a promising direction for future innovations in automated diagnostic systems and intelligent healthcare technologies.

5. RESULT

It is our great pleasure to present the paper for our project titled "Deep Learning for MRI Image Classification using CNN".

ACKNOWLEDGEMENT

I would like to express profound gratitude to my guide Dr. H P Mohan Kumar, Professor & HOD, Department of CS, PES College of Engineering, Mandya for his continues guidance and support to publish my research paper successfully.

REFERENCES [1]Cmak Zeelan Basha; A Likhitha; P Alekhya; V Aparna, “Computerised Classification of MRI Images using Machine Learning Algorithms”, IEEE, 04 August 2020.

[2] Meghavi Rana & Megha Bhushan, “Machine learning and deep learning approach for medical image analysis: diagnosis to detection”, Springer Nature Link, Volume 82,24 December 2022.

Chart -1: Accuracy of Dataset

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BIOGRAPHIES

[3] Jianpeng Zhang , Yutong Xie , Qi Wu , Yong Xia , “Medical image classification using synergic deep learning”, Science Direct, Volume 54, May 2019.

Chandana T R, received her Bachelor’s degree from Mandya University Mandya, and she currently pursuing MCA in PES College of Engineering Mandya,

[4] Destiny Agboro, “Medical Image Classification with Machine Learning Classifier”, Predictive Analytics with KNIME 2023.

[5] X. Xie, Deep learning-based image classification of MRI brain image, ResearchGate (College of Medicine and Biological Information Engineering, Northeastern University), 2021.

[6] A. Elbachari et al., MRI Brain Images Classification Using Convolutional Neural Networks, published via Springer in AI2SD (part of Lecture Notes in Artificial Intelligence), 2020.

Dr. H P Mohan Kumar, obtained MCA, MSc Tech and PhD from University od Mysore, India in 1998, 2009 and 2015 respectively.

He is working as a Professor in department of CS, PES College of Engineering, Mandya, Karnataka, India, His area of interest are Computer Vision, and Data Mining.

[7] P. Mahesh et al., Enhancing brain tumor classification in MRI scans with a multi-layer CNN, Frontiers in Computational Neuroscience, 2024.

[8] Customized CNN with SHAP, LIME, Grad-CAM authors, Utilizing customized CNN for brain tumor prediction with explainable AI, ScienceDirect (Journal), 2024.

[9] CNN-TumorNet team, Leveraging explainability in deep learning for precise brain tumor classification (CNN-TumorNet), Frontiers in Oncology, 2025.

[10] Francisco Javier Díaz-Pernas, Mario Martínez-Zarzuela, Míriam Antón-Rodríguez, David González-Ortega, A Deep Learning Approach for Brain Tumor Classification and Segmentation Using a Multiscale Convolutional Neural Network, arXiv (also cross-posted for peer review), 2024.

[11] Team at MDPI (advanced classification), Advanced Brain Tumor Classification in MR Images Using Transfer Learning (Xception, DenseNet201, EfficientNetB5), MDPI (Cancers journal), 2024–2025.

[12] Study in BMC Medical Informatics and Decision Making, MRI-based brain tumor detection using convolutional deep learning, BMC Med Inform Decis Mak, 2023.

[13] Authors of automated deep learning framework, An automated deep learning framework for brain tumor classification, Scientific Reports (Nature Publishing), 2025.

[14] Authors of Ensemble technique, Ensemble Deep Learning Technique for Detecting MRI Brain Tumor, Wiley / Hindawi, 2024.

[15] Application of Deep Learning in Detecting Neurological Disorders from MRI (2020, Brain Informatics review) Surveys CNN-based architectures (VGG16, InceptionV4, ResNet) for Alzheimer’s detection and other neuro disorders.

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