Skin Disease Classification Using Machine Learning Algorithms

Skin Disease Classification using Machine Learning Algorithms Thesis Topics

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1. Skin Disease Classification using Machine Learning based Proposed Ensemble Model

Keywords:

Naive Bayes, Support Vector Machines, Skin Disease, Acne, Ensemble method, Max Voting Scheme, Classification

            Our paper concentrates on using ML methods to classify various types of skin diseases. We use four different data mining methods like SVM, KNN, RF and NB. We use the ensemble method i.e. the grouping of SVM, KNN, RF and NB by utilizing it as a voting system. Our suggested method classifies the skin disease into five various modules are acne, skin allergy, nail fungus, hair loss and normal skin. Our proposed ensemble model performed better than other classification models.   

2. Development of Intelligent Skin Disease Classification system using Machine Learning

Keywords:

Principal Component Analysis, Dimensionality reduction, Machine Learning

            Our paper suggested an automated model for extremely accurate classification of skin lesions. We also join the eight classification methods namely SVM, KNN, RF, DT, NB, Gradient Boosting classifier and LR with dimensionality reduction method PCA. We also used clustering methods based on PCA to reduce the feature extraction. Our proposed model performs the high accuracy.

3. Analysis of Automated Skin Disease Classification Exploiting Different Machine Learning Techniques

Keywords:

Deep Learning, Computer Vision, Pattern Recognition, Image Processing

            Our work proposes a ML based automated system to classify the skin disease into three individual methods namely melanoma, basal cell carcinoma and eczema. We use various Machine Learning methods like SVM, NB, LR and Deep Learning methods like CNN, LSTM, Bi-LSTM, Inception V3, VGG-16, and Xception. The Deep learning method Xception achieved the maximum accuracy.  

4. Skin Disease Classification Using Machine Learning Algorithms

Keywords:

Feature extraction

            The aim of our paper is to increase the diagnosis of skin disease. Our paper uses colour and texture to classify the skin disease. In our paper we used Hue-Saturation-Value (HSV) features like entropy, variance and maximum histogram values. We also used the ML techniques like Support Vector Machine. At first level we used entropy to distribute the tree and next the variance should be used to get leaves for texturing. Our proposed method gives the best accuracy rate.

5. Comparative Analysis of Machine Learning Approaches for Classifying Erythemato-Squamous Skin Diseases

Keywords:

Erythemato-Squamous Diseases, Classification, Comparative Analysis

            Our paper uses ML methods to predict the type of Erythemato-Squamous the skin disease. We use the ML methods like SVM, DT, RF, KNN, NB, Gradient Boosting, XGBoost and Multilayer perception are popular in protect state information through classification/segmentation. RF, Gradient Boosting and XGBoost performs best accuracy.

6. OESV-KRF: Optimal ensemble support vector kernel random forest based early detection and classification of skin diseases

Keywords:

Skin cancer, Prediction, Segmentation, Ensemble support vector machine kernel random forest, Hybrid equilibrium Aquila optimization algorithm

            Our paper uses ensemble support vector kernel random forest-based hybrid equilibrium Aquila optimization (ESVMKRF-HEAO) to accurately and correctly predict the skin disease at early stage. First, we can remove the noise in the dataset, and next the image qualities were improved using preprocessing methods. To segment the lesion area threshold-based segmentation method can be used. Then our suggested method accurately detects and classifies the segmented images based on their feature characters.  

7. Skin Disease Classification Using Machine Learning and Data Mining Algorithms

Keywords:

Ensemble data Mining techniques

            In our paper we use ML methods for segmentation and diagnosis. We can extract the features from photos as their input. It is difficult to choose the corresponding feature extraction method with corresponding ML technique. Ensemble data mining and ML methods can be utilized to classify the skin disease. We utilized four separate method to classify the different type of disease when ensemble methods used to improve the classification reliability.  

8. A Model for Classification and Diagnosis of Skin Disease using Machine Learning and Image Processing Techniques

Keywords:

Diagnosis, RF, K-NN, cherry angioma, melanoma, psoriasis

            The improvement of ML and image processing technique helps to detect the skin disease earlier. Our paper offers a method that get an image of the skin affected by the disease. Our paper uses five steps that are image acquisition, preprocessing, segmentation, feature extraction and classification. We used the ML techniques like SVM, RF and KNN classifiers. SVM predicts the better outcome.

9. Image-based Skin Disease Detection and Classification through Bioinspired Machine Learning Approaches

Keywords:

Neural networks, Bioinspired Machine Learning (Bio-ML), Swarm intelligence.

            We used NN and Swarm Intelligence (SI) based methods are used to classify and diagnose the skin disease. Our paper uses CNN based Cuckoo Search Algorithm (CS) that can be trained by utilizing the multi-objective optimization technique cuckoo search. CNN-CS model is calculated by contrast the three metaheuristic-based classifiers: CNN-GA, CNN-BAT, and CNN-PSO.

10. Classification and Detection of Skin Disease Based on Machine Learning and Image Processing Evolutionary Models

Keywords:

Skin disorders, image enhancement, image segmentation, disease detection

            Our paper uses an evolutionary model for skin disease classification and detection based on ML and image processing techniques. We incorporate the methods like image processing, image augmentation, segmentation and ML. We used the ML methods like SVM, KNN and RF for image classification and detection. Our SVM model outperforms the best accuracy rate.