Highly Accurate Brain Tumor Segmentation and Classification Using Multiple Feature Sets
Purpose: Nowadays, detecting brain tumors is a crucial application. If a tumor is discovered later on, the medical issues are significant. Therefore, early diagnosis is essential. Magnetic Resonance Imaging (MRI) is the most recent detection, diagnosis, and assessment technology. Materials and M...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Tehran University of Medical Sciences
2025-07-01
|
| Series: | Frontiers in Biomedical Technologies |
| Subjects: | |
| Online Access: | https://fbt.tums.ac.ir/index.php/fbt/article/view/659 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850032607696257024 |
|---|---|
| author | Megha Sunil Borse Murali Prasad R Tummala Ranga Babu |
| author_facet | Megha Sunil Borse Murali Prasad R Tummala Ranga Babu |
| author_sort | Megha Sunil Borse |
| collection | DOAJ |
| description |
Purpose: Nowadays, detecting brain tumors is a crucial application. If a tumor is discovered later on, the medical issues are significant. Therefore, early diagnosis is essential. Magnetic Resonance Imaging (MRI) is the most recent detection, diagnosis, and assessment technology.
Materials and Methods: In this study, MRI images are segmented before input to a pulse-coupled neural network model to identify the existence of a tumor in the brain picture. The doctor may turn to this model for assistance if there are more input MRI brain pictures. This work preprocesses the images using normalization smoothing with linear filter and adaptive histogram. Statistical and Local Binary Patterns (LBP) features are extracted from the preprocessed images to perform the classification process. The Deep Convolutional Network (DCNN) is used to segment the image. The Pulse Coupled Neural Networks (PCNN) categorize the input images as normal and tumor.
Results: Accuracy, sensitivity, specificity, and precision are the various metrics evaluated. This work achieves 99.35 accuracies, 99.78 sensitivity, 98.45 specificities, and 97.61 precision. This work is compared with previous implementations to measure performance.
Conclusion: The comparison analysis improves tumor segmentation and classification accuracy. The suggested method yields great outcomes.
|
| format | Article |
| id | doaj-art-e899e3a1d2e24becbd361efbb0725a65 |
| institution | DOAJ |
| issn | 2345-5837 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Tehran University of Medical Sciences |
| record_format | Article |
| series | Frontiers in Biomedical Technologies |
| spelling | doaj-art-e899e3a1d2e24becbd361efbb0725a652025-08-20T02:58:34ZengTehran University of Medical SciencesFrontiers in Biomedical Technologies2345-58372025-07-0112310.18502/fbt.v12i3.19183Highly Accurate Brain Tumor Segmentation and Classification Using Multiple Feature SetsMegha Sunil Borse0Murali Prasad R1Tummala Ranga Babu2Department of Electronics and Telecommunication Engineering, Cummins College of Engineering for Women, Karvenagar, Pune, IndiaDepartment of Electronics and Communication Engineering, Marri Laxman Reddy Institute of Technology and Management, Dundigal, Hyderabad, IndiaElectronics & Communication Engineering, Rayapati Venkata Rangarao & Jagarlamudi Chandramouli College of Engineering, Guntur, India Purpose: Nowadays, detecting brain tumors is a crucial application. If a tumor is discovered later on, the medical issues are significant. Therefore, early diagnosis is essential. Magnetic Resonance Imaging (MRI) is the most recent detection, diagnosis, and assessment technology. Materials and Methods: In this study, MRI images are segmented before input to a pulse-coupled neural network model to identify the existence of a tumor in the brain picture. The doctor may turn to this model for assistance if there are more input MRI brain pictures. This work preprocesses the images using normalization smoothing with linear filter and adaptive histogram. Statistical and Local Binary Patterns (LBP) features are extracted from the preprocessed images to perform the classification process. The Deep Convolutional Network (DCNN) is used to segment the image. The Pulse Coupled Neural Networks (PCNN) categorize the input images as normal and tumor. Results: Accuracy, sensitivity, specificity, and precision are the various metrics evaluated. This work achieves 99.35 accuracies, 99.78 sensitivity, 98.45 specificities, and 97.61 precision. This work is compared with previous implementations to measure performance. Conclusion: The comparison analysis improves tumor segmentation and classification accuracy. The suggested method yields great outcomes. https://fbt.tums.ac.ir/index.php/fbt/article/view/659Brain TumorAdaptive Histogram EqualizationPulse Coupled Neural NetworksDeep Convolutional NetworkLocal Binary Patterns |
| spellingShingle | Megha Sunil Borse Murali Prasad R Tummala Ranga Babu Highly Accurate Brain Tumor Segmentation and Classification Using Multiple Feature Sets Frontiers in Biomedical Technologies Brain Tumor Adaptive Histogram Equalization Pulse Coupled Neural Networks Deep Convolutional Network Local Binary Patterns |
| title | Highly Accurate Brain Tumor Segmentation and Classification Using Multiple Feature Sets |
| title_full | Highly Accurate Brain Tumor Segmentation and Classification Using Multiple Feature Sets |
| title_fullStr | Highly Accurate Brain Tumor Segmentation and Classification Using Multiple Feature Sets |
| title_full_unstemmed | Highly Accurate Brain Tumor Segmentation and Classification Using Multiple Feature Sets |
| title_short | Highly Accurate Brain Tumor Segmentation and Classification Using Multiple Feature Sets |
| title_sort | highly accurate brain tumor segmentation and classification using multiple feature sets |
| topic | Brain Tumor Adaptive Histogram Equalization Pulse Coupled Neural Networks Deep Convolutional Network Local Binary Patterns |
| url | https://fbt.tums.ac.ir/index.php/fbt/article/view/659 |
| work_keys_str_mv | AT meghasunilborse highlyaccuratebraintumorsegmentationandclassificationusingmultiplefeaturesets AT muraliprasadr highlyaccuratebraintumorsegmentationandclassificationusingmultiplefeaturesets AT tummalarangababu highlyaccuratebraintumorsegmentationandclassificationusingmultiplefeaturesets |