Table 2 Performance Analysis of Neurological Disorders

Nazir, et al. (2019)

Convolutional neural network (CNN)

Automatic deep features

CT images

Specificity 99%, sensitivity 95%, and accuracy 98% claimed

No benchmark datasets were experimented. Only local dataset used. Difficult to generalize results

Amin et al. (2020a)

SVM and 3D–2D hybrid CNN combination

Spectral and Spatial features

vivo HS brain cancer dataset

80% accuracy attained

No benchmark dataset was experimented. Also accuracy is low

Saba et al. (2018b)

2-level histogram-based morphometry (HBM)

3D histogram of oriented gradients (HOG)

sMRI

(AUC) of > 0.75 in each dataset, highest AUC of 0.849 in case of ETH site

Traditional machine learning used that is not suitable for large datasets. Also accuracy is low

Ma et al. (2022)

Normal Pressure Hydrocephalus segmentation and classification (NPH-SC) model and CCN

Skull features and segmentation by marker-based watershed approach

MRI Brain

NPH-SC model: sensitivity 96%, specificity 100%, validation accuracy 97%. CNN test accuracy 98%

Experiments are performed on small dataset & validity of the approach seems not good on large dataset

Mathur et al. (2020)

CNN

geometric data augmentation techniques

MRIs

Accuracy 98.8% and specificity 99%

No benchmark dataset used & experiments performed on augmented data

Jabeen et al. (2018)

Feedforward multilayer neural network

PCA + ANN

MRIs

Accuracy 75%

Small dataset employed for experiments