Machine Learning Algorithms based Skin Disease Detection

Bhadula, Shuchi; Sharma, Sachin; Juyal, Piyush; Kulshrestha, Chitransh

doi:10.35940/ijitee.b7686.129219

Cited by 53 publications

(16 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…They combined the highlights from the Filter and Color Names features to achieve the best classification result, which was 52.19%. Shuchi et al [13] described a comparison study using five different machine learning algorithms to detect skin diseases. The proposed system has been created and approved using data from almost 3000 image tests.…”

Section: Literature Reviewmentioning

confidence: 99%

Computer Vision Based Skin Disease Recognition

Saha

Majumder

2023

Preprint

View full text Add to dashboard Cite

All over the world, various types of skin diseases are affecting an increasing number of people. It is incredibly simple to diagnose diseases with the aid of modern medical technology. However, there are times when people are unable to quickly travel to a hospital or diagnostic facility and it is also extremely expensive for most people, particularly in developing nations like Bangladesh. It is a good idea to have a backup plan in case something goes wrong, but it's not always possible. This is primarily an image processing method. Then total twelve features have been extracted from our all datasets consist of five diseases (Actinic keratosis, Benign, Eczema, Malignant, Psoriasis). Then, to learn our model, four different machine learning algorithms have utilized. For the psoriasis disease, the highest accuracy from the Random Forest classifier has obtained, which is 88%, and the F1-score is 96%. Therefore, this model can accurately recognize skin diseases with great effectiveness.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

Computer Vision Based Skin Disease Recognition

Saha

Majumder

2023

Preprint

View full text Add to dashboard Cite

show abstract

“…The scientific article authored by Shuchi Bhadula, Sachin Sharma, Piyush Juyal, Chitransh Kulshresth et al [2] provides an extensive review of the use of machine learning algorithms for precise identification and classification of various skin disorders. To determine the exact type of skin diseases, the scientists investigated five different algorithms, including "Kernel SVM", "Logistic Regression", "Random Forest", "CNN" and "Naive Bayes".…”

Section: Literature Reviewmentioning

confidence: 99%

Ai Empowered Diagnosis of Pemphigus: A Machine Learning Approach for Automated Skin Lesion Detection

Ahmed,

Islam,

Alif

et al. 2023

IAPGOS

View full text Add to dashboard Cite

Pemphigus is a skin disease that can cause a serious damage to human skin. Pemphigus can result in other issues including painful patches and infected blisters, which can result in sepsis, weight loss, and starvation, all of which can be life-threatening, tooth decay and gum disease. Early prediction of Pemphigus may save us from fatal disease. Machine learning has the potential to offer a highly efficient approach for decision-making and precise forecasting. The healthcare sector is experiencing remarkable advancements through the utilization of machine learning techniques. Therefore, to identify Pemphigus using images, we suggested machine learning-based techniques. This proposed system uses a large dataset collected from various web sources to detect Pemphigus. Augmentation has been applied on our dataset using techniques such as zoom, flip, brightness, distortion, magnitude, height, width to enhance the breadth and variety of the dataset and improve model’s performance. Five popular machine learning algorithms has been employed to train and evaluate model, these are K-Nearest Neighbor (referred to as KNN), Decision Tree (DT), Logistic Regression (LR), Random Forest (RF), and Convolutional Neural Network (CNN). Our outcome indicate that the CNN based model outperformed the other algorithms by achieving accuracy of 93% whereas LR, KNN, RF and DT achieved accuracies of 78%, 70%, 85% and 75% respectively.

show abstract

“…They used random forest, kernel SVM, Naïve Bayes, Logistic Regression, and Convolution Neural Network algorithms. Finally from the confusion matrix, they discovered that the convolution neural network model gave the best result for this disease detection process [4].…”

Section: Literature Reviewmentioning

confidence: 99%

Skin Disease Detection: Machine Learning vs Deep Learning

Bandyopadhyay¹,

Bhaumik²,

Poddar³

2021

Preprint

View full text Add to dashboard Cite

Skin disease is a very common disease for humans. In the medical industry detecting skin disease and recognizing its type is a very challenging task. Due to the complexity of human skin texture and the visual closeness effect of the diseases, sometimes it is really difficult to detect the exact type. Therefore, it is necessary to detect and recognize the skin disease at its very first observation. In today's era, artificial intelligence (AI) is rapidly growing in medical fields. Different machine learning (ML) and deep learning(DL) algorithms are used for diagnostic purposes. These methods drastically improve the diagnosis process and also speed up the process. In this paper, a brief comparison between the machine learning process and the deep learning process was discussed. In both processes, three different and popular algorithms are used. For the machine Learning process Bagged Tree Ensemble, K-Nearest Neighbor (KNN), and Support Vector Machine(SVM) algorithms were used. For the deep learning process three pre-trained deep neural network models

show abstract

Machine Learning Algorithms based Skin Disease Detection

Cited by 53 publications

References 15 publications

Computer Vision Based Skin Disease Recognition

Computer Vision Based Skin Disease Recognition

Ai Empowered Diagnosis of Pemphigus: A Machine Learning Approach for Automated Skin Lesion Detection

Skin Disease Detection: Machine Learning vs Deep Learning

Contact Info

Product

Resources

About