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BTC-fCNN: Fast Convolution Neural Network for Multi-class Brain Tumor Classification

计算机科学 Softmax函数 人工智能 卷积神经网络 卷积(计算机科学) 学习迁移 模式识别(心理学) 人工神经网络 上下文图像分类 深度学习 机器学习 图像(数学)
作者
Basant S. Abd El-Wahab,Mohamed E. Nasr,S. Khamis,Amira S. Ashour
出处
期刊:Health information science and systems [Springer Nature]
卷期号:11 (1) 被引量:33
标识
DOI:10.1007/s13755-022-00203-w
摘要

Abstract Timely prognosis of brain tumors has a crucial role for powerful healthcare of remedy-making plans. Manual classification of the brain tumors in magnetic resonance imaging (MRI) images is a challenging task, which relies on the experienced radiologists to identify and classify the brain tumor. Automated classification of different brain tumors is significant based on designing computer-aided diagnosis (CAD) systems. Existing classification methods suffer from unsatisfactory performance and/or large computational cost/ time. This paper proposed a fast and efficient classification process, called BTC-fCNN, which is a deep learning-based system to distinguish between different views of three brain tumor types, namely meningioma, glioma, and pituitary tumors. The proposed system’s model was applied on MRI images from the Figshare dataset. It consists of 13 layers with few trainable parameters involving convolution layer, 1 × 1 convolution layer, average pooling, fully connected layer, and softmax layer. Five iterations including transfer learning and five-fold cross-validation for retraining are considered to increase the proposed model performance. The proposed model achieved 98.63% average accuracy, using five iterations with transfer learning, and 98.86% using retrained five-fold cross-validation (internal transfer learning between the folds). Various evaluation metrics were measured to evaluate the proposed model, such as precision, F-score, recall, specificity and confusion matrix. The proposed BTC-fCNN model outstrips the state-of-the-art and other well-known convolution neural networks (CNN).
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