Skin disease classification using machine learning (ML) offer fast and early diagnoses, aiding dermatologists and developing patient results. We have framed many research topics and have given research ideas to scholars so that they outshine in their academics. Being the world’s no.1 leading concern for all research enquires we have satisfied more than 4000+ customers worldwide. Our skilled machine learning writers have a specific style of framework where their vocabulary and writing style plays crucial step for approval of your research work.
The following is step-by-step process which we design a skin disease classification project:
We define the aim of our project is to develop a ML framework that perfectly classify various skin diseases based on skin lesion pictures.
Utilization of ML for skin disease classification, we offer beneficial techniques to enlarge dermatological practice, improving the speed and possibly the accuracy of diagnosis in limited resource settings. We know that our model should often use as an assistive tool and not replacing the expert’s medical advice.
Simply contact us for all your research enquires and notify in advance to finish off your work efficiently. Your work will be handled only by machine learning professionals who possess extensive knowledge in that specific subject area be rest assured you are in safe hands.
The best thesis topics will be given from the current high reputed journal of that current year. Our thesis writers maintain open channels of communication to scholars so that it is well written as per their needs. Timely delivery of your thesis work will be done. More over if you want thesis editing service our team also accommodates it.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
Before sit down to research proposal writing, we need to decide exact journals. For e.g. SCI, SCI-E, ISI, SCOPUS.
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To ensure the novelty of research, we find research gaps in 50+ latest benchmark papers (IEEE, Springer, Elsevier, MDPI, Hindawi, etc.)
After literature survey, we get the main issue/problem that your research topic will aim to resolve and elegant writing support to identify relevance of the issue.
Based on the research gaps finding and importance of your research, we conclude the appropriate and specific problem statement.
Writing a good research proposal has need of lot of time. We only span a few to cover all major aspects (reference papers collection, deficiency finding, drawing system architecture, highlights novelty)
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We get the approval for implementation tool, software, programing language and finally implementation plan to start development process.
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For every project order, we deliver the following: reference papers, source codes screenshots, project video, installation and running procedures.
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We play crucial role in this step since this is very important for scholar’s future. Our experts will help you in choosing high Impact Factor (SJR) journals for publishing.
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Paper published in online and we inform you with paper title, authors information, journal name volume, issue number, page number, and DOI link
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