RETRACTED ARTICLE: A novel method for prediction of skin disease through supervised classification techniques

Meena, K.; Veni, N. N. Krishna; Deepapriya, B. S.; Vardhini, P. A. Harsha; Kalyani, B. J. D.; Sharmila, L.

doi:10.1007/s00500-022-07435-8

Cited by 7 publications

(4 citation statements)

References 24 publications

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“…The reason for this step is to obtain the optimal predictive performance of a model [ 47 ]. Accuracy, precision, recall, and f1-score were calculated to analyze the algorithms’ performance and select the best algorithm [ 48 ]. To identify whether our model is an underfit or overfit model, we created learning curves on accuracy for training and validation data using k-fold cross-validation to train and test data.…”

Section: Methodsmentioning

confidence: 99%

WebGIS-Based Real-Time Surveillance and Response System for Vector-Borne Infectious Diseases

Javaid

Sarfraz

Aftab

et al. 2023

IJERPH

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The diseases transmitted through vectors such as mosquitoes are named vector-borne diseases (VBDs), such as malaria, dengue, and leishmaniasis. Malaria spreads by a vector named Anopheles mosquitos. Dengue is transmitted through the bite of the female vector Aedes aegypti or Aedes albopictus mosquito. The female Phlebotomine sandfly is the vector that transmits leishmaniasis. The best way to control VBDs is to identify breeding sites for their vectors. This can be efficiently accomplished by the Geographical Information System (GIS). The objective was to find the relation between climatic factors (temperature, humidity, and precipitation) to identify breeding sites for these vectors. Our data contained imbalance classes, so data oversampling of different sizes was created. The machine learning models used were Light Gradient Boosting Machine, Random Forest, Decision Tree, Support Vector Machine, and Multi-Layer Perceptron for model training. Their results were compared and analyzed to select the best model for disease prediction in Punjab, Pakistan. Random Forest was the selected model with 93.97% accuracy. Accuracy was measured using an F score, precision, or recall. Temperature, precipitation, and specific humidity significantly affect the spread of dengue, malaria, and leishmaniasis. A user-friendly web-based GIS platform was also developed for concerned citizens and policymakers.

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Section: Methodsmentioning

confidence: 99%

WebGIS-Based Real-Time Surveillance and Response System for Vector-Borne Infectious Diseases

Javaid

Sarfraz

Aftab

et al. 2023

IJERPH

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“…Multiple studies have highlighted the pivotal role of machine learning and image processing in advancing the precision and efficiency of diagnosing skin diseases, signaling a shift towards the integration of AI in the field of dermatology [19][20][21][22][23][24][25]. For example, AlDera and Ben Othman [19] innovated a diagnostic model for conditions like acne and melanoma, implementing a comprehensive process encompassing image acquisition, preprocessing, segmentation, feature extraction, and classification, and achieved impressive accuracy with algorithms such as SVM, RF, and KNN.…”

Section: The Glance Of Machine Learningmentioning

confidence: 99%

“…Huong, Khang, Quynh, Thang, Canh and Sang [23] proposed a sophisticated deep and machine learning hybrid model for classifying monkeypox, obtaining high accuracy and F1-score. Meena, Veni, Deepapriya, Vardhini, Kalyani and Sharmila [25] experimented with KNN, SVM, and RF classifiers for skin disease data, emphasizing feature selection and real-time dataset performance. Finally, MunishKhanna, Singh and Garg [24] adopted a natureinspired computing strategy for predicting various human diseases, including skin cancer, leveraging ant-lion optimization for feature selection and achieving significant accuracy across diverse datasets.…”

Section: The Glance Of Machine Learningmentioning

confidence: 99%

SkinLiTE: Lightweight Supervised Contrastive Learning Model for Enhanced Skin Lesion Detection and Disease Typification in Dermoscopic Images

Alzahrani

2024

Preprint

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This study introduces SkinLiTE, a lightweight supervised contrastive learning model, tailored to enhance the detection and typification of skin lesions in dermoscopic images. The core of SkinLiTE lies in its unique integration of supervised and contrastive learning approaches, which leverages labeled data to learn generalizable representations. This approach is particularly adept at handling the complexities and imbalances inherent in skin lesions datasets. The methodology encompasses a two-phase learning process. In the first phase, SkinLiTE utilizes an encoder network and a projection head to transform and project dermoscopic images into a feature space where contrastive loss is applied, focusing on minimizing intra-class variations while maximizing inter-class differences. The second phase freezes the encoder's weights, leveraging the learned representations for classification through a series of dense and dropout layers. The model was evaluated using three datasets from Skin Cancer ISIC 2019-2020, covering a wide range of skin conditions. SkinLiTE demonstrated superior performance across various metrics—accuracy, AUC , and F1 score—particularly when compared with traditional supervised learning models. Notably, SkinLiTE achieved an accuracy of 0.9087 using AugMix augmentation for binary classification of skin lesions. It also showed comparable results with the state-of-the-art approaches of ISIC challenge without relying on external data, underscoring its efficacy and efficiency. The results highlight the potential of SkinLiTE as a significant step forward in the field of dermatological AI, offering a robust, efficient, and accurate tool for skin lesion detection and classification. Its lightweight architecture and ability to handle imbalanced datasets make it particularly suited for integration into Internet of Medical Things environments, paving the way for enhanced remote patient monitoring and diagnostic capabilities. This research contributes to the evolving landscape of AI in healthcare, demonstrating the impact of innovative learning methodologies in medical image analysis.

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“…In recent years, deep learning techniques have gained significant attention in various fields such as Pattern Recognition [1][2][3][4][5][6][7][8][9], Medical Imaging, Video Analysis, driver drowsiness detection [10,11], video analysis, Spam detection [12], Healthcare, Clustering [13] and many more. One of the popular techniques is transfer learning, which allows the pretrained models to be used for a new set of tasks with minimal training data.…”

Section: Introductionmentioning

confidence: 99%

Pokepedia: Pokemon Image Classification Using Transfer Learning

Chirra,

Syeda,

Kolla

et al. 2023

RCES

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Identifying images of various objects, living creatures, food, etc., and classifying them using machine learning has become a common task in computer vision. Humans may not identify every object they see, here comes machine learning that eases the life of human beings by identifying the object for the human. Poké mon is a cartoon that is widely watched by the majority of the younger generation around the world. The aim of this work to predict and classify Poké mon images using pre-trained models. In the proposed work, seven pre-trained models namely MobileNetV2, EfficientNetB7, EfficientNetV2L, DenseNet201, ResNet101, VGG19 and VGG16 were utilised to classify ten Poké mon characters which includes Pikachu, Raichu, Charmander, Bulbasaur, Squirtle, Eevee, Piplup, Snorlax, Jigglypuff, and Psyduck. The performance of the pre-trained models were evaluated on a dataset collected from the internet. The ResNet101 pre-trained model produces the highest accuracy of 95.60% when compared with the other models.

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RETRACTED ARTICLE: A novel method for prediction of skin disease through supervised classification techniques

Cited by 7 publications

References 24 publications

WebGIS-Based Real-Time Surveillance and Response System for Vector-Borne Infectious Diseases

WebGIS-Based Real-Time Surveillance and Response System for Vector-Borne Infectious Diseases

SkinLiTE: Lightweight Supervised Contrastive Learning Model for Enhanced Skin Lesion Detection and Disease Typification in Dermoscopic Images

Pokepedia: Pokemon Image Classification Using Transfer Learning

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