Table 1 Gist of existing works related to polymicrogyria.

⁶

PPMR dataset

The CDCM loss function is used to classify PMG using ResNet50

Recall—88.07, Precision – 71.86

⁷

Embryonic brain dataset

Deep features are extracted and classified using SVM classifier

Accuracy – 87.7

⁴

Internal dataset, National Institute of Health (NIH) pediatric brain MRI database, and the Developing Human Connectome Project (dHCP) database

Combining 2D and 3D CNN into an ensemble to predict myelin maturation age. 3D CNN from⁴⁶ and EfficientNet-b0 as the 2D CNN

Mean Absolute Error (MAE) Results:

Cross-validation set: 2D model – 1.53, 3D model – 2.06, Ensemble model – 1.63

Internal test set: 2D model – 1.43, 3D model – 2.55, Ensemble model – 1.77

External NIH dataset: 2D model – 2.26, 3D model – 2.27, Ensemble model – 1.22

External dHCP dataset: 2D model – 0.44, 3D model – 0.27, Ensemble model – 0.31

³²

Publicly available Brain Tumor MRI dataset

Transfer learning is adopted to evaluate and compare multiple pre-trained deep learning models, such as, VGG-16, Inception-v3, and ResNet50

Accuracies of VGG16 – 96, InceptionV3 – 78, ResNet50 – 95

Precision of VGG16 – 94, InceptionV3 – 75, ResNet50 – 92

Recall of VGG16 – 100, InceptionV3 – 70, ResNet50 – 89

F1-score of VGG16 – 98, InceptionV3 – 73, ResNet50 – 94

³⁴

Custom MRI dataset collected and augmented by Swati Kanchan from NIT Durgapur

Image resizing and normalization techniques are adopted. Transfer learning and fine-tuning of MobileNet CNN are utilized. GradCam is experimented for visual explanation

Validation Accuracy: 97.24; Test Accuracy: 97.86; Precision: 97.91; Recall: 97.86; F1-score: 97.86 for four class classification