Optimizing MRI-based brain tumor classification and detection using AI: A comparative analysis of neural networks, transfer learning, data augmentation, and the cross-transformer network

Abstract

Early detection and diagnosis of brain tumors are crucial to taking adequate preventive measures, as with most cancers. On the other hand, artificial intelligence (AI) has grown exponentially, even in such complex environments as medicine. Here it's proposed a framework to explore state-of-the-art deep learning architectures for brain tumor classification and detection. An own development called Cross-Transformer is also included, which consists of three scalar products that combine self-care model keys, queries, and values. Initially, we focused on the classification of three types of tumors: glioma, meningioma, and pituitary. With the Figshare brain tumor dataset was trained the InceptionResNetV2, InceptionV3, DenseNet121, Xception, ResNet50V2, VGG19, and EfficientNetB7 networks. Over 97 % of classifications were accurate in this experiment, which provided a network's performance overview. Subsequently, we focused on tumor detection using the Brain MRI Images for Brain Tumor Detection and The Cancer Genome Atlas Low-Grade Glioma database. The development encompasses learning transfer, data augmentation, as well as image acquisition sequences; T1-weighted images (T1WI), T1-weighted post-gadolinium (T1-Gd), and Fluid-Attenuated Inversion Recovery (FLAIR). Based on the results, using learning transfer and data augmentation increased accuracy by up to 6 %, with a p-value below the significance level of 0.05. As well, the FLAIR sequence was the most efficient for detection. As an alternative, our proposed model proved to be the most effective in terms of training time, using approximately half the time of the second fastest network.

Keywords: Artificial intelligence; Cancer detection; Machine learning; Magnetic resonance imaging; Transformers; Tumors.

Fig. 1

Samples of A) the three types of brain tumors in the BTD database, B) the MRI-D database with the two classes: tumors and non-tumors, and C) the TCGA-LGG database with the three types of sequences and in the two classes: tumors and non-tumors.

Fig. 2

Score distribution generated by the different training evaluated with the test data. A) accuracy, B) sensitivity, and C) specificity. D) metrics as a function of tumor type for the best performing network (InceptionResNetv2).

Fig. 3

Average training and 95 % error bands for the best performing network (InceptionResNetv2). A) accuracy as a function of epochs and B) loss as a function of epochs with training and validation data.

Fig. 4

Scores distribution generated by the different training evaluated with the test data. A) Accuracy, B) Sensitivity, C) Specificity, and D) F1 score. The distributions are shown for the four training conditions, i.e., without any strategy, learning transfer, data augmentation, and combining learning transfer and data augmentation. MRI-D dataset.

Fig. 5

Metrics as a function of the two classes in tumor detection with MRI-D data.

Fig. 6

Average training and 95 % error bands for the best performing network (InceptionResNetV2). A) Accuracy as a function of epochs and B) Loss as a function of epochs with the training and validation data. Tumor detection experiment with transfer learning and data augmentation with the MRI-D database.

Fig. 7

Scores distribution generated by the different training evaluated with the test data. A) Accuracy, B) Sensitivity, C) Specificity, and D) F1 score. The distributions are shown for the three image acquisition sequences, i.e., T1WI, FLAIR, and T1-Gd. TCGA-LGG dataset.

Fig. 8

Average training and 95 % error bands for the best performing network InceptionResNetV2. A) Accuracy as a function of epochs and B) Loss as a function of epochs, with training and validation data. Tumor detection experiment with the FLAIR sequence on TCGA-LGG dataset.

Fig. 9

Average training and 95 % error bands for the proposed network (Cross-Transformer). A) Accuracy as a function of epochs and B) Loss as a function of epochs, with training and validation data. Tumor detection experiment with the FLAIR sequence on TCGA-LGG dataset.

Fig. 10

The average training time of the 8 architectures with 95 % confidence interval (black lines). Training.