AUTHOR

AIMS

SAMPLE

METHODS

MAIN FINDINGS

Warin et al. [8],

(2022)

Evaluated CNN algorithms, classify and detect OPMDs in oral photographs

600 oral photographs images (300 images of OPMDs and 300 images of normal oral mucosa)

Assessed the most suitable CNN-based classification and detection algorithm models for OPMD detection.

·        DenseNet-121 and ResNet-50 are excellent for categorising OPMDs.

·        YOLOv4 and Faster RCNN performed better in detecting lesions on oral photographic images.

Chen W et al. [18], (2022)

Estimated cancer risk from oral precancerous lesions using ANN-assisted cancer risk prediction approach.

230 sample size used in ratio of 7:3 for training & testing sets

BP algorithm was used for predicting the risk of oral cancer in OPMDs cases.

_·             Accuracy rate of ANN based model algorithm is more than 90%.

Phosri K et al. [19], (2022)

Developed classification system from 10 comparable pre trained transfer models on oral cavity photographs of white lesions, ulcer and normal mucosa.

200 images of each oral white lesions, ulcer and normal anatomy

Ten pre-trained transfer learning models were implemented and assessed DenseNet121, DenseNet169, DenseNet201, Xception, ResNet50, InceptionResNetV2, InceptionV3, VGG16, VGG19, and EfficientNetB7.

·        Recall of 0.8833 and was performed by the trained models of DenseNet169, DenseNet201, and Xception.

·        The DenseNet169 performed better than other models in terms of precision, F51score, and specificity, scoring 0.9034, 0.884, and 0.9417, respectively.

Lin H et al. [17],

(2021)

To overcome the difficulties of automatic detection of oral disorders and provide an efficient smartphone-based image diagnosis technique powered by a DL algorithm.

455 cases(228 normal, 76 apthous ulcer,69 low risk OPMDs,52 high risk OPMDs and 30 oral cancers)

·        Retrospective study

·        Oral cavity images were collected by centered rule image capturing approach

·        DL network (HRNet) was introduced to evaluate the performance for oral cancer detection.

·        Performance attained a sensitivity of 83.0%, specificity of 96.6%, accuracy of 84.3%, and F1 of 83.6%.

·        "Centre positioning" method performed about 8% better than a simulated "random positioning" method, the resampling method performed 6% better.

·        HRNet performed marginally better than VGG16, ResNet50, and DenseNet169 in terms of sensitivity, specificity, precision, and F1 score.

Jucryszyn et al. [20], (2020)

A useful texture analysis algorithm for diagnosing oral leukoplakia

35 sample size of leukoplakia patient

Run length matrix and co-occurrence matrix, two textural properties were examined by using ANN, factors were analysed and classes determined.

·        Leukoplakia and normal mucosa may be easily distinguished with sensitivity 100% and specificity 97%