Techniques (Reference number is mentioned)

Segmentation

Feature extraction

Features

Classification

Advantages

Limitations

Accuracy

[16]

Pixel based-Region growing

color and textural features

Extracted features are mean, standard deviation, variation, skewness, angular second moment, contrast, correlation, the sum of variance, inverse difference moment, sum average, sum variance, sum entropy, entropy, difference variance, difference entropy, information measures of correlation, and maximal correlation coefficient

SVM + kNN

The performance is high with the fusion of the SVM-kNN classifier.

Dataset is not standard and the feature extraction method is not defined.

98%

[20]

-

Rough Set-Based Feature Selection

Features are erythema, scaling, borders, itching, koebner phenomenon, polygonal and follicular papules, oral mucosal involvement, knee-elbow, and scalp involvement, family history, age, etc.

SVM + KNN + MLP

System accuracy is robust with a faster response time.

The segmentation algorithm is not defined.

97.78%

[22]

Threshold-based Adaptive Snake (AS) approach

ABCD rule + Epiluminescence microscopy (ELM) criteria algorithm

Features extracted in ABCD rule with ELM criteria.

GA + SVM with Radial Basis Function (RBF)

GA lessens the element measurements

to improve framework execution.

The segmentation method is not working properly which decreases system accuracy.

88%

[23]

-

-

-

Multi-level CNN + BVLC Alexnet model

Easy to understand and low complexity system.

Poor efficiency of the system with a lower recall and precision rate.

The segmentation and feature extraction method is not defined.

70%

[29]

-

Canny edge detection

The CED method detects sharp edges with image boundaries.

GA + BPNN

Cloud computing-based skin disease diagnosis system deals with enormous amounts of datasets.

Efficient segmentation is absent and system accuracy is also undefined.

-

[36]

-

Pretrained convolutional neural network (AlexNet)

Max pooling criteria evolve with 5-layer convolutional architecture.

Deep convolution neural network + ECOC (Error-correcting output codes) linear SVM

The smart master system enhances existing work with an expansion inexactness of 3.21% in the confusion matrix.

Poor system performance due to proper segmentation approach is undefined.

86.21%