A Hybrid Deep Learning Model with Data Augmentation to Improve Tumor Classification Using MRI Images
Background: Cancer ranks second among the causes of mortality worldwide, following cardiovascular diseases. Brain cancer, in particular, has the lowest survival rate of any form of cancer. Brain tumors vary in their morphology, texture, and location, which determine their classification. The accurat...
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MDPI AG
2024-11-01
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| Online Access: | https://www.mdpi.com/2075-4418/14/23/2710 |
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| author | Eman M. G. Younis Mahmoud N. Mahmoud Abdullah M. Albarrak Ibrahim A. Ibrahim |
| author_facet | Eman M. G. Younis Mahmoud N. Mahmoud Abdullah M. Albarrak Ibrahim A. Ibrahim |
| author_sort | Eman M. G. Younis |
| collection | DOAJ |
| description | Background: Cancer ranks second among the causes of mortality worldwide, following cardiovascular diseases. Brain cancer, in particular, has the lowest survival rate of any form of cancer. Brain tumors vary in their morphology, texture, and location, which determine their classification. The accurate diagnosis of tumors enables physicians to select the optimal treatment strategies and potentially prolong patients’ lives. Researchers who have implemented deep learning models for the diagnosis of diseases in recent years have largely focused on deep neural network optimization to enhance their performance. This involves implementing models with the best performance and incorporating various network architectures by configuring their hyperparameters. Methods: This paper presents a novel hybrid approach for improved brain tumor classification by combining CNNs and EfficientNetV2B3 for feature extraction, followed by K-nearest neighbors (KNN) for classification, which has been described as one of the simplest machine learning algorithms based on supervised learning techniques. The KNN algorithm assumes similarities between new cases and available cases and assigns new cases to the category that most closely resembles the available categories. Results: To evaluate the recommended method’s efficacy, two widely known benchmark MRI datasets were utilized in the experiments. The initial dataset consisted of 3064 MRI images depicting meningiomas, gliomas, and pituitary tumors. Images from two classes, consisting of healthy brains and brain tumors, were included in the second dataset, which was obtained from Kaggle. Conclusions: In order to enhance the performance even further, this study concatenated the CNN and EfficientNetV2B3’s flattened outputs before feeding them into the KNN classifier. The proposed framework was run on these two different datasets and demonstrated outstanding performance, with accuracy of 99.51% and 99.8%, respectively. |
| format | Article |
| id | doaj-art-b085a62c26734005978027b8fd3688db |
| institution | Kabale University |
| issn | 2075-4418 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Diagnostics |
| spelling | doaj-art-b085a62c26734005978027b8fd3688db2024-12-13T16:24:45ZengMDPI AGDiagnostics2075-44182024-11-011423271010.3390/diagnostics14232710A Hybrid Deep Learning Model with Data Augmentation to Improve Tumor Classification Using MRI ImagesEman M. G. Younis0Mahmoud N. Mahmoud1Abdullah M. Albarrak2Ibrahim A. Ibrahim3Faculty of Computers and Information, Minia University, Minia 61519, EgyptFaculty of Computers and Information, Minia University, Minia 61519, EgyptCollege of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 13318, Saudi ArabiaFaculty of Computers and Information, Minia University, Minia 61519, EgyptBackground: Cancer ranks second among the causes of mortality worldwide, following cardiovascular diseases. Brain cancer, in particular, has the lowest survival rate of any form of cancer. Brain tumors vary in their morphology, texture, and location, which determine their classification. The accurate diagnosis of tumors enables physicians to select the optimal treatment strategies and potentially prolong patients’ lives. Researchers who have implemented deep learning models for the diagnosis of diseases in recent years have largely focused on deep neural network optimization to enhance their performance. This involves implementing models with the best performance and incorporating various network architectures by configuring their hyperparameters. Methods: This paper presents a novel hybrid approach for improved brain tumor classification by combining CNNs and EfficientNetV2B3 for feature extraction, followed by K-nearest neighbors (KNN) for classification, which has been described as one of the simplest machine learning algorithms based on supervised learning techniques. The KNN algorithm assumes similarities between new cases and available cases and assigns new cases to the category that most closely resembles the available categories. Results: To evaluate the recommended method’s efficacy, two widely known benchmark MRI datasets were utilized in the experiments. The initial dataset consisted of 3064 MRI images depicting meningiomas, gliomas, and pituitary tumors. Images from two classes, consisting of healthy brains and brain tumors, were included in the second dataset, which was obtained from Kaggle. Conclusions: In order to enhance the performance even further, this study concatenated the CNN and EfficientNetV2B3’s flattened outputs before feeding them into the KNN classifier. The proposed framework was run on these two different datasets and demonstrated outstanding performance, with accuracy of 99.51% and 99.8%, respectively.https://www.mdpi.com/2075-4418/14/23/2710brain tumorCNNEfficientNetV2B3KNN |
| spellingShingle | Eman M. G. Younis Mahmoud N. Mahmoud Abdullah M. Albarrak Ibrahim A. Ibrahim A Hybrid Deep Learning Model with Data Augmentation to Improve Tumor Classification Using MRI Images Diagnostics brain tumor CNN EfficientNetV2B3 KNN |
| title | A Hybrid Deep Learning Model with Data Augmentation to Improve Tumor Classification Using MRI Images |
| title_full | A Hybrid Deep Learning Model with Data Augmentation to Improve Tumor Classification Using MRI Images |
| title_fullStr | A Hybrid Deep Learning Model with Data Augmentation to Improve Tumor Classification Using MRI Images |
| title_full_unstemmed | A Hybrid Deep Learning Model with Data Augmentation to Improve Tumor Classification Using MRI Images |
| title_short | A Hybrid Deep Learning Model with Data Augmentation to Improve Tumor Classification Using MRI Images |
| title_sort | hybrid deep learning model with data augmentation to improve tumor classification using mri images |
| topic | brain tumor CNN EfficientNetV2B3 KNN |
| url | https://www.mdpi.com/2075-4418/14/23/2710 |
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