Abstract

Brain tumor is a deadly disease and its classification is a challenging task for radiologists because of the heterogeneous nature of the tumor cells. Recently, computer-aided diagnosis-based systems have promised, as an assistive technology, to diagnose the brain tumor, through magnetic resonance imaging (MRI). In recent applications of pre-trained models, normally features are extracted from bottom layers which are different from natural images to medical images. To overcome this problem, this study proposes a method of multi-level features extraction and concatenation for early diagnosis of brain tumor. Two pre-trained deep learning models i.e. Inception-v3 and DensNet201 make this model valid. With the help of these two models, two different scenarios of brain tumor detection and its classification were evaluated. First, the features from different Inception modules were extracted from pre-trained Inception-v3 model and concatenated these features for brain tumor classification. Then, these features were passed to softmax classifier to classify the brain tumor. Second, pre-trained DensNet201 was used to extract features from various DensNet blocks. Then, these features were concatenated and passed to softmax classifier to classify the brain tumor. Both scenarios were evaluated with the help of three-class brain tumor dataset that is available publicly. The proposed method produced 99.34 %, and 99.51% testing accuracies respectively with Inception-v3 and DensNet201 on testing samples and achieved highest performance in the detection of brain tumor. As results indicated, the proposed method based on features concatenation using pre-trained models outperformed as compared to existing state-of-the-art deep learning and machine learning based methods for brain tumor classification.

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Key Takeaways

• A novel deep learning model improves brain tumor classification using MRI images.
• The model employs a concatenation approach with pre-trained networks Inception-v3 and DenseNet201 for feature extraction.
• Utilizes a dataset of 3064 T1-weighted contrast MR images representing glioma, meningioma, and pituitary tumors.
• Achieves high classification accuracies of 99.34% (Inception-v3) and 99.51% (DenseNet201).
• The proposed method significantly outperforms existing state-of-the-art classification techniques.
• Highlights the potential of deep learning in enhancing diagnostic accuracy in medical imaging.
• Future research should focus on fine-tuning and data augmentation strategies for further performance improvements.