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.
Highlights
The advances in biomedical and human intelligence have overcome diverse diseases in the last few years but people are still, suffering from cancer due to its unpredictable nature
We have removed most of Inception module at bottom layers of pre-trained Inception-v3 deep learning model and concatenated features at the bottom layers of proposed models for classification based on brain tumor dataset
This paper discussed the application of deep learning models for the identification of brain tumor
Summary
The advances in biomedical and human intelligence have overcome diverse diseases in the last few years but people are still, suffering from cancer due to its unpredictable nature. N. Noreen et al.: Deep Learning Model Based on Concatenation Approach for the Diagnosis of Brain Tumor some cells are developed normally, some decrease their capabilities, stop their growth, and become abnormal. The brain tumor analyzation, classification, and identification are critical issues for a neurologist who is using CAD (computer-aided diagnosis) as a supportive tool for a medical operation. Some studies have employed deep learning to improve the performance of computer-aided medical diagnosis to investigate the brain tumor cancer. The less accuracy in prediction models and critical nature of the medical data analyzation force researchers toward new methods of brain tumor detection to improve classification accuracy. The motivation to use pre-trained deep learnings is time saving, because it does not need a large data set to obtain results These models extracted random features from images for classification. Last section concludes the whole discussion and present the future work
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