Techniques (Reference number is

mentioned)

Segmentation

Characteristics

Feature extraction

Classification

Advantages

Limitations

Accuracy

[15]

K-means clustering + Otsu method

Images are clustered with the threshold value.

GLCM

BPNN

An NN-based system that is in a real-time embedded manner.

Computational complexity is high due to it works in real time environment.

95.83%

[21]

Otsu Thresholding method + Edge-based Morphological operations

Edge-based segmentation with threshold values.

2-D Fast Fourier Transform, 2-D Discrete Cosine

Transform, Complexity Feature Set, and Color Feature Set

SVM

An enriched filtering algorithm helps all other stages to perform efficiently.

Well defined feature extraction algorithm is required to enhance accuracy.

93.50%

[27]

The active contour method + Marker control watershed algorithm.

The output of the active contour is used as an input to the marker control watershed algorithm

GLCM

SVM

A CAD system enhances the detection and classification and reduces the time latency.

An NN-based classification approach is needed.

94%

[30]

Uniform distribution based segmentation + Active contour method

Four metrics: DICE, Jaccard Index, Jaccard Difference, and Diameter are calculated for each segmented image.

Color features

SVM

It is a robust skin disease detection system with value.

Reliable and low complexity based feature extraction method is required.

97.5%

[31]

Edge-based segmentation + ACM

The active contours method works with sharp edges.

Sobel Operator

BPNN

An NN-based method that successfully detects several diseases.

An efficient hybrid feature extraction algorithm is needed.

90%

[32]

Otsu thresholding method + Morphological operations: dilation, erosion

A morphological operation performs on pixels.

ABCD rule

-

Almost all of the melanoma images are correctly identified using morphological operations.

Without a trained neural network, diseases are not classified properly.

-

[37]

ACO + GA algorithm

Initialize GA and ACO parameters. Segmentation accuracy is 94%.

GLCM

Transductive Support Vector Machine (TSVM)

Better segmentation accuracy gives high performance. At first, this system is predicted as 24 diseases with fitness function.

Hybrid feature extraction is required.

95%