Skin Disease Identification Research Articles

Skin Disease Analysis and Prediction using Machine Learning

A number of patients all over the world suffer from skin diseases; detection in the early stages is very important for curing such disease. For diagnosing skin conditions however, several factors make this very difficult, these include the multitude of skin diseases, their similar symptoms and dependence on expert dermatological evaluation. In this case, adopting machine learning (ML) is quite a suitable approach as it can help improve the accuracy of diagnosis and its availability. Therefore, the focus of the project is on the developing of a machine learning based skin disease detection system.The problem in consideration is the high demand for efficient and effective mechanisms that can be used in skin disease identification/ detection, whereby such tools should also be able to be used in low income areas. To tackle this, we use a machine learning model which classifies different forms of skin diseases through the use of dermatoscopic images.

GAN Based Multi-Class Skin Disease Classification: Deep Learning Approach

Abstract: Skin diseases pose significant diagnostic and treatment challenges due to their diverse and complex manifestations. Convolutional neural networks (CNNs) have demonstrated superior capabilities in image classification tasks, including skin disease identification. However, the performance of CNN models depends heavily on the quality and quantity of training data, which often suffers from limitations such as imbalance and sparsity. This project proposes an approach new approach to address these challenges by integrating generative adversarial networks (GANs). ) with CNN for multi-class skin disease classification. The GAN-based system aims to improve the diversity and quantity of the training dataset by generating synthetic images of various skin conditions. Through an adversarial training process, the generator network learns to generate realistic images of skin diseases, while the discriminator network distinguishes between real and synthetic data. Figures The synthetic images generated by the GAN are then combined with the real dataset to train the model CNN. specially designed to classify skin diseases.

Skin Disease Detection and Remedial System

With the advent of smart processing systems in computers it has driven the emergence of inventive solutions within health-care. One notable instance is the Skin Disease Detection and Recommendation System, utilizing AI and machine learning methods to elevate dermatological diagnosis and treatment guidance. This summary offers a comprehensive overview of the Skin Disease Detection System, outlining its core elements, methodologies, advantages, and potential healthcare impact. The System for Skin Disease Detection aims to transform dermatology by automating the skin disease identification process and delivering customized treatment suggestions. This also aims to detect the skin types and suggest remedial medication and other things for the same. Consulting with a dermatologist is also easy by this. Employing image processing, pattern recognition, and deep learning algorithms, this system accurately evaluates skin condition images. The solution's application was developed using Streamlit, Python, PHP, Bootstrap, and MySQL.

A Method of Skin Disease Detection Using Image Processing and Machine Learning

Skin conditions are prevalent health issues globally. The dangers posed by these infections are unseen, leading to physical discomfort and mental anguish. In severe cases, skin diseases can even progress to skin cancer. Consequently, the identification of skin diseases from clinical images remains a significant challenge in medical image analysis. Furthermore, manual diagnosis by medical professionals is time-consuming and subjective. Therefore, there is a need for automated skin disease prediction to expedite treatment planning for both patients and dermatologists. These studies presents a digital hair removal method and de-blurs or denoise the images. To extract essential patterns from the skin imagesand statistical features techniques are utilized. Two efficient machine learning algorithms, namely Support Vector Machine (SVM), and Convolutional Neural Network (CNN) classifiers, are employed with the extracted features to accurately classify skin images as melanoma (MEL), melanocytic nevus (NV), basal cell carcinoma (BCC), actinic keratosis (AK), benign keratosis (BKL), dermatofibroma (DF), vascular lesion (VASC), and Squamous cell carcinoma (SCC). The models are validated using the ISIC 2019 challenge and HAM10000 datasets, with SVM demonstrating slightly superior performance compared to the other classifiers. Furthermore, a comparison with state-of-the-art methods is conducted to evaluate the effectiveness of the proposed approach.

A Literature Survey on BIO-PULSE AI based Medcare App

The MedCare Chatbot signifies a significant leap forward in healthcare technology, providing users with individualized medical guidance and prescription suggestions. Going beyond its primary functions, this inventive chatbot incorporates a pioneering feature centered on the identification of skin diseases through image processing techniques. Utilizing the capabilities of artificial intelligence and computer vision, the MedCare Chatbot can analyse images of skin conditions submitted by users, furnishing precise diagnoses and treatment recommendations. This paper details the design, implementation, and assessment of this advanced functionality seamlessly integrated into the MedCare Chatbot. Employing a dual strategy of symptom-based prescription advice and image-based skin disease detection, the MedCare Chatbot aims to enhance healthcare accessibility and empower users to actively manage their well-being

Machine Learning Study: Identification of Skin Diseases for Various Skin Types Using Image Classification.

Increased machine learning methods have helped improvise human interaction with digital devices which helps in skin disease identification, prediction, and classification by employing algorithms. Image classification for skin disease application algorithms can detect caucasian skin tones but poorly performs when analyzing other skin colors. In this research, a deep learning algorithm was used to address the problem that other applications perform poorly with the classification of skin disease types.
 Convolutional Neural Network (CNN), a machine-learning algorithm was used to classify images and add the predicted images within the data set. The images in the data set covered a lot of patient factors such as age, sex, disease site (hand, feet, head, nails, etc.), skin color (white, yellow, brown, black) and different periods of lesions (early, middle, or late). Multiple private applications can detect skin diseases during the analysis. For the darker color skin population, the performance was poor, and skin cancer detection was not possible even with the help of image recognition.
 This research aims to conduct an analysis of visual searches within skin-related health searches to identify opportunities to provide digital health consumers with visual search results that are more representative of America’s diverse populations.

Automated Identification of Cutaneous Leishmaniasis Lesions Using Deep-Learning-Based Artificial Intelligence.

The polymorphism of cutaneous leishmaniasis (CL) complicates diagnosis in health care services because lesions may be confused with other dermatoses such as sporotrichosis, paracocidiocomycosis, and venous insufficiency. Automated identification of skin diseases based on deep learning (DL) has been applied to assist diagnosis. In this study, we evaluated the performance of AlexNet, a DL algorithm, to identify pictures of CL lesions in patients from Midwest Brazil. We used a set of 2458 pictures (up to 10 of each lesion) obtained from patients treated between 2015 and 2022 in the Leishmaniasis Clinic at the University Hospital of Brasilia. We divided the picture database into training (80%), internal validation (10%), and testing sets (10%), and trained and tested AlexNet to identify pictures of CL lesions. We performed three simulations and trained AlexNet to differentiate CL from 26 other dermatoses (e.g., chromomycosis, ecthyma, venous insufficiency). We obtained an average accuracy of 95.04% (Confidence Interval 95%: 93.81-96.04), indicating an excellent performance of AlexNet in identifying pictures of CL lesions. We conclude that automated CL identification using AlexNet has the potential to assist clinicians in diagnosing skin lesions. These results contribute to the development of a mobile application to assist in the diagnosis of CL in health care services.

Improved Skin Disease Classification with Mask R-CNN and Augmented Dataset

Skin diseases are a significant global health concern, impacting millions worldwide. Severe diseases like psoriasis and dermatitis can coexist with more benign skin issues like acne and eczema. Primary care physicians in tropical areas often treat patients with skin issues in locations where onchocerciasis and tinea imbricate are prominent, such infections might even take center stage. Usually, skin illnesses are disregarded medically and considered cosmetic, but they can have serious psychosocial effects, especially at an early age, and very few global studies have attempted to quantify the frequency of skin diseases. Nevertheless, the ability to make an accurate diagnosis at an early stage is crucial for successful treatment of complex diseases. However, Skin disease identification is a complex process. We introduce a state-of-the-art approach that uses Mask R-CNN in conjunction with an augmented dataset from the HAM10000 from the Harvard University Open Data Repository to achieve close to 80% accuracy in skin disease classification. We provide an in-depth analysis of our approach, covering data preprocessing, model architecture, training, and evaluation, along with detailed tables presenting training and testing results and associated hyperparameters.

Exploring the Potential of Convolutional Neural Networks in Healthcare Engineering for Skin Disease Identification

Skin disorders affect millions of individuals worldwide, underscoring the urgency of swift and accurate detection for optimal treatment outcomes. Convolutional Neural Networks (CNNs) have emerged as valuable assets for automating the identification of skin ailments. This paper conducts an exhaustive examination of the latest advancements in CNN-driven skin condition detection. Within dermatological applications, CNNs proficiently analyze intricate visual motifs and extricate distinctive features from skin imaging datasets. By undergoing training on extensive data repositories, CNNs proficiently classify an array of skin maladies such as melanoma, psoriasis, eczema, and acne. The paper spotlights pivotal progressions in CNN-centered skin ailment diagnosis, encompassing diverse CNN architectures, refinement methodologies, and data augmentation tactics. Moreover, the integration of transfer learning and ensemble approaches has further amplified the efficacy of CNN models. Despite their substantial potential, there exist pertinent challenges. The comprehensive portrayal of skin afflictions and the mitigation of biases mandate access to extensive and varied data pools. The quest for comprehending the decision-making processes propelling CNN models remains an ongoing endeavor. Ethical quandaries like algorithmic predisposition and data privacy also warrant significant consideration. By meticulously scrutinizing the evolutions, obstacles, and potential of CNN-oriented skin disorder diagnosis, this critique provides invaluable insights to researchers and medical professionals. It underscores the importance of precise and efficacious diagnostic instruments in ameliorating patient outcomes and curbing healthcare expenditures.

Identification of Skin Disease using Deep Learning Model

Busy lifestyles these days have led people to forget to drink water regularly which results in inadequate hydration and oily skin, oily skin has become one of the main factors for Acne vulgaris. Acne vulgaris, particularly on the face, greatly affects a person’s social, mental wellbeing and personal satisfaction for teens. Besides the fact that acne is well known as an inflammatory disorder, it was reported to have caused serious long-term consequences such as depression, scarring, mental illness, including pain and suicide. In the present work a deep learning model is designed for the identification of skin diseases.

A novel two‐staged network for skin disease detection using atrous residual convolutional networks

SummaryThe preferred modality for the identification of skin disease has predominantly been dermoscopy. Skin lesion identification is the primary diagnostic basis for the dermatologist to evaluate the severity and impact of the disease. Current diagnostic procedures for the identification of skin lesions may be subjected to misdiagnosis among observers. Also, these procedures are conventionally labour‐intensive and prone to delays in treatment. Deep learning has proven to be beneficial for automated computer‐aided diagnosis in the medical field. This research presents a two‐stage approach involving segmentation and classification architectures for the effective detection of skin disease. The proposed approach aims to combine the architectural benefits of residual learning with the contextual retention of atrous convolutions. The classification is performed on seven different classes of skin lesions namely, melanoma (MEL), melanocytic nevus (NV), basal cell carcinoma (BCC), actinic keratosis/Bowen's disease (AKIEC), benign keratosis (BKL), dermatofibroma (DF), and vascular lesion (VASC). To the best of our knowledge, this is the first research that reports the impact of a two‐staged detection network using atrous residual convolutions for these seven classes. Experimental observations indicate that the proposed model is characteristically balanced in terms of inter‐class performance, with precise segmentation. The proposed network was trained with the HAM10000 dataset and reported a classification accuracy and precision of 89.27% and 89.06%, respectively.

Penerapan Artificial Intelligence Untuk Klasifikasi Penyakit Kulit Dengan Metode Convolutional Neural Network Berbasis Web

Skin is one of the human organs that functions to regulate body temperature in humans, as well as to protect all organs in the human body. There are many factors that affect skin health conditions that cause skin diseases. A system was developed to help people detect skin diseases. This system is Artificial Intelligence with Convolutional Neural Network method so that it will produce a very significant image. The network will be trained to find angles, edges, shapes, and also features. The results of system performance in this study using adam optimizer with a learning rate of 0.0001 get the highest value of data accuracy reaching a value of 97%. So that the identification of skin diseases is quite good.

Skin Disease Classification Using Mobilenet-RseSK Network

The traditional deep learning method has a large amount of calculation, long time training and complex network structure, which is not easy to be applied to embedded or mobile devices. To solve the these problems, we proposed an improved lightweight mobile network named as Mobilenet-RseSK for skin disease classification. Firstly, a new attention mechanism seSK module is proposed, and seSK module is used to replace the position of SE in the original network. This module can better perform feature extraction and improve network performance than the original network attention module. Secondly, using RBN normalization, RBN maintains the advantages of BN, and strengthens the representation of specific features, which strengthens the degree of accuracy of skin disease identification. We compare the Mobilenet-RseSK network with MobilenetV3, Ghost and other advanced networks on the HAM10000 dataset. The proposed network promotes the accuracy of skin disease classification by 1.7% compared with the original network. Compared with advanced mobilenet network, our method achieves 85% accuracy on the test set. This network has certain practical value in skin disease classification, and is an effective lightweight skin disease classification algorithm.

Analysis of Skin disease techniques using Smart Phone and Digital Camera Identification of Skin Disease

Skin diseases are a serious health issue that affects a large number of individuals. In recent years, with the fast advancement of technology and the use of various data mining approaches, dermatological predictive classification has become increasingly predictive and accurate. It is more help to dermatologist to identify the disease, As a result, the development of machine learning approaches capable of efficiently. The purpose of this study is making an application of identification skin disease images by using the machines learning method, Support Vector Machine (SVM), and KNN techniques. The image processes and machine learning is performed early detection of skin diseases. The aim of this study is determined the classification of skin diseases in humans. Each skin disease has symptoms. It has five skin diseases such as Acne, Psoriasis, Wrath, Psoriasis, and Ulcer. We have collected 314 skin disease images from the government of hospital, Aurangabad with the help of mobile camera and Sony HD camera. Gaussian Filter is used for image pre-processing. The segmentation method is used for K-Means Clustering and the feature extraction method are used for feature extraction. We have used Haar feature, color feature, FCM, OS-FCM, GLCM and LBF features for classifications. Based on the result, the SVM is given 92% accuracy for haar feature, FCM and OS-FCM. and KNN classifier, K-Means are given 89% and 89% accuracy using mobile phone camera dataset. The SVM, KNN and K-Means are given 91%, 87% and 89% accuracy respectively using Sony HD camera dataset. SVM is given good result in both dataset.

Expert System for Identification of Skin Disease in Humans using Naive Bayes Classifier Method on Web

Expert System Identification of Skin Disease in Human is an application that adopts expert knowledge, in this case, a dermatologist, to identify skin diseases in humans found in Indonesia. This expert system is constructed using a naive bayes classifier. To make the system interface more dynamic and easy to access by anyone, this system is made based on the web. Through this web application, users can consult with a system like an expert to know the skin disease that occurs to the human and find the correct treatment to solve the problems.

Hand Image Based Skin Disease Identification Using Machine Learning and Deep Learning Algorithms

The skin disease is most commonly affected to all peoples. It is a big challenging process in skin image identification because of the combined texture shape with the color variations. This paper presented to identify a skin using texture features (Local Binary Pattern and Gray Level Co-occurance Matrix) and color features with various learning models. The proposed work technique for identification of skin disease has two stages: either extracted feature values and identification. During the first stage, texture features and color feature vector values are taken from the images in a dataset of hand skin database. During the second stage, the extracted feature vectors are trained by the various kinds of Machine Learning models. This hand skin image dataset also applied into Deep Learning model for identification. The performance measures of the system is evaluated using accuracy measure. The Convolutional Neural Network (CNN) model gives accuracy of 87.5% when compared to Machine Learning models.

A convolutional neural network for skin cancer classification

<span>Skin diseases can be seen clearly by oneself and others. Although this disease is visible on the skin, sometimes we worry if this skin disease is not mild. Some people experience skin diseases directly and quickly go to a dermatologist to have their complaints and symptoms checked. This skin protects the body, especially from the sun, so it can lead to death if something goes wrong. One example of a skin disease that can be deadly is skin cancer or skin tumors. In this research, we classified skin cancer into Benign and Malignant using the convolution neural network (CNN) algorithm. The purpose of this research is to develop the CNN architecture to help identify skin diseases. We used a dataset of 3,297 skin cancer images which are publicly available on the Kaggle website. We propose two CNN architectures that differ in the number of parameters. The first architecture has 6,427,745 parameters, and the second architecture has 2,797,665. With both architectures, the accuracy of the first model is 93%, and the second model is 74%. The first model with the number of parameters 6,427,745 We save for use in the creation of the website. We created a web-based application with the Django framework for skin disease identification.</span>

Computational Technique Based on Machine Learning and Image Processing for Medical Image Analysis of Breast Cancer Diagnosis

Breast cancer is the most lethal type of cancer for all women worldwide. At the moment, there are no effective techniques for preventing or curing breast cancer, as the source of the disease is unclear. Early diagnosis is a highly successful means of detecting and managing breast cancer, and early identification may result in a greater likelihood of complete recovery. Mammography is the most effective method of detecting breast cancer early. Additionally, this instrument enables the detection of additional illnesses and may provide information about the nature of cancer, such as benign, malignant, or normal. This article discusses an evolutionary approach for classifying and detecting breast cancer that is based on machine learning and image processing. This model combines image preprocessing, feature extraction, feature selection, and machine learning techniques to aid in the classification and identification of skin diseases. To enhance the image’s quality, a geometric mean filter is used. AlexNet is used for extracting features. Feature selection is performed using the relief algorithm. For disease categorization and detection, the model makes use of the machine learning techniques such as least square support vector machine, KNN, random forest, and Naïve Bayes. The experimental investigation makes use of MIAS data collection. This proposed technology is advantageous for accurately identifying breast cancer disease using image analysis.

Computed Aided Diagnosis System for Skin Disease Identification

Skin disease cases are expanding every day and hard to deal with because of the worldwide unevenness among skin disease patients and dermatologists, Skin infections are among the main 5 motivating reasons for the overall disease trouble, to decrease, CAD frameworks are greatly required, single skin disease characterization is the significant inadequacy in the current work. The characterization of various skin diseases is exceptionally tested because of the comparative attributes of skin illnesses. The pattern of skin infections will group skin condition details into one of the six non-covering groups such as healthy, acne, eczema, psoriasis, benign and malignant melanoma, etc. Four phases compose the proposed order structure, pre-processing, segmentation, feature extraction, and classification. To accomplish each stage, various digital image processing and machine learning techniques are used and achieving 90 to 95% accuracy using a Support Vector Machine. Result of CAD technique also supported to the doctor easily find out a detected area as compared to other techniques.

CBI + R: A Fusion Approach to Assist Dermatological Diagnoses

With the emerge of advanced technologies such as high-resolution cameras and computational power, it seems to ease to built a better dermatological diagnostic system. However, the identification of skin disease is still a challenging problem with the origination of various skin diseases. In this paper, we proposed a new fusion architecture — CBI [Formula: see text] R to support the diagnosis in multiple skin diseases. The architecture combines Content-Based Image Retrieval (CBIR) and Case-Based Reasoning (CBR) technology together to facilitate medical diagnosis. CBIR is to retrieve digital dermoscopy images from a data repository using the shape, texture and color features. Along with these features, CBR is incorporated which contains symptoms, case history and treatment plan of the disease. Experiments on a set of 1210 images yielded an accuracy of 98.2%. This was a superior retrieval and diagnosis performance in comparison with the state-of-the-art works.