A novel nonlinear automated multi-class skin lesion detection system using soft-attention based convolutional neural networks

Alhudhaif, Adi; ِAlmaslukh, Bandar Saleh Mouhammed; Aseeri, Ahmad O.; Güler, Osman; Polat, Kemal

doi:10.1016/j.chaos.2023.113409

Cited by 18 publications

(4 citation statements)

References 42 publications

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“…Their findings underscored the efficacy of augmentation in enhancing classification performance, yielding notable improvements in accuracy and F-scores while mitigating false negatives. In a separate study, Alhudhaif et al [21] proposed a deep learning approach incorporating attention mechanisms and supported by data balancing techniques. Leveraging the HAM10000 dataset comprising 10,015 labeled images across seven distinct skin lesion types, they initially observed accuracy rates of 85.73% for training, 70.90% for validation, and 69.75% for testing on the unbalanced dataset.…”

Section: Introductionmentioning

confidence: 99%

RETRACTED: Utilizing Generative Adversarial Networks for Acne Dataset Generation in Dermatology

Sankar,

Chaturvedi,

Nayan

et al. 2024

BioMedInformatics

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Background: In recent years, computer-aided diagnosis for skin conditions has made significant strides, primarily driven by artificial intelligence (AI) solutions. However, despite this progress, the efficiency of AI-enabled systems remains hindered by the scarcity of high-quality and large-scale datasets, primarily due to privacy concerns. Methods: This research circumvents privacy issues associated with real-world acne datasets by creating a synthetic dataset of human faces with varying acne severity levels (mild, moderate, and severe) using Generative Adversarial Networks (GANs). Further, three object detection models—YOLOv5, YOLOv8, and Detectron2—are used to evaluate the efficacy of the augmented dataset for detecting acne. Results: Integrating StyleGAN with these models, the results demonstrate the mean average precision (mAP) scores: YOLOv5: 73.5%, YOLOv8: 73.6%, and Detectron2: 37.7%. These scores surpass the mAP achieved without GANs. Conclusions: This study underscores the effectiveness of GANs in generating synthetic facial acne images and emphasizes the importance of utilizing GANs and convolutional neural network (CNN) models for accurate acne detection.

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Section: Introductionmentioning

confidence: 99%

RETRACTED: Utilizing Generative Adversarial Networks for Acne Dataset Generation in Dermatology

Sankar,

Chaturvedi,

Nayan

et al. 2024

BioMedInformatics

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“…Alhudhaif et al [55] recommended a deep learning approach that was based on mechanisms for focusing attention and enhanced by methods for balancing data. The dataset used in the study was HAM10000, which included 10,015 annotated skin images of seven different types of skin lesions.…”

Section: Machine Learning and Deep Learning In Skin Disease Classific...mentioning

confidence: 99%

Skin Lesion Classification and Detection Using Machine Learning Techniques: A Systematic Review

Debelee

2023

Diagnostics

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Skin lesions are essential for the early detection and management of a number of dermatological disorders. Learning-based methods for skin lesion analysis have drawn much attention lately because of improvements in computer vision and machine learning techniques. A review of the most-recent methods for skin lesion classification, segmentation, and detection is presented in this survey paper. The significance of skin lesion analysis in healthcare and the difficulties of physical inspection are discussed in this survey paper. The review of state-of-the-art papers targeting skin lesion classification is then covered in depth with the goal of correctly identifying the type of skin lesion from dermoscopic, macroscopic, and other lesion image formats. The contribution and limitations of various techniques used in the selected study papers, including deep learning architectures and conventional machine learning methods, are examined. The survey then looks into study papers focused on skin lesion segmentation and detection techniques that aimed to identify the precise borders of skin lesions and classify them accordingly. These techniques make it easier to conduct subsequent analyses and allow for precise measurements and quantitative evaluations. The survey paper discusses well-known segmentation algorithms, including deep-learning-based, graph-based, and region-based ones. The difficulties, datasets, and evaluation metrics particular to skin lesion segmentation are also discussed. Throughout the survey, notable datasets, benchmark challenges, and evaluation metrics relevant to skin lesion analysis are highlighted, providing a comprehensive overview of the field. The paper concludes with a summary of the major trends, challenges, and potential future directions in skin lesion classification, segmentation, and detection, aiming to inspire further advancements in this critical domain of dermatological research.

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“…Oversampling is a technique for balancing data distribution by increasing the distribution of low data to the same as other high data distributions [34] [35]. ROS is an oversampling technique that balances data distribution by randomly taking data until it meets the data needed to balance it [36]. The research uses the SMOTE oversampling technique against the NB and SVM algorithms.…”

Section: Introductionmentioning

confidence: 99%

“…Then other studies compare the SMOTE, adaptive synthetic (ADASYN), ROS, and data augmentation oversampling techniques. SMOTE has an accuracy value of 95.94%, train 99.86%, and validation of 96.41% [36]. Contributions in this study are: i) applying sentiment analysis related to the recession, ii) the dataset used in this research is Indonesian language tweets data, especially on news portal accounts, iii) comparing popular classification algorithms, namely NB, SVM, and KNN, in classifying sentiment, iv) comparing labeling techniques such as VADER and TextBlob related to the recession in sentiment analysis, v) overcoming data imbalance using oversampling techniques, such as SMOTE and ROS.…”

Section: Introductionmentioning

confidence: 99%

Dealing imbalance dataset problem in sentiment analysis of recession in Indonesia

Kristiyanti,

Sanjaya,

Tjokro

et al. 2024

IJ-AI

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<span lang="EN-US">Global recession news dominates social media, particularly in Indonesia, with social news platforms on Twitter generating public responses and <br /> re-tweetings on the issue. Mining these opinions from Twitter using a sentiment analysis approach yields invaluable insights. The research stages included data collection, pre-processing, data labeling using the <br /> lexical-based method like valence aware dictionary and sentiment reasoner (VADER) and TextBlob, sampling techniques using synthetic minority oversampling technique (SMOTE) and random over sampling (ROS) before and after splitting data, and modeling using machine learning such as support vector machines (SVM), k-nearest neighbour (KNN), naive Bayes, and model evaluation. The problem is that almost 300,000 data collected from NodeXL are unbalanced. The findings show that models with balanced datasets show better model evaluation results. The sampling technique was carried out before and after splitting the data. The model evaluation results show that the Bernoulli-naive Bayes algorithm, with the VADER labeling technique, and the SMOTE sampling technique after splitting data, obtains the best accuracy of 84%, and using the ROS technique obtains an accuracy of 81%. On the other hand, with the SMOTE and ROS technique before splitting data on the SVM algorithm, it gets the best accuracy of 93% from before if only using SVM only reached 84%.</span>

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A novel nonlinear automated multi-class skin lesion detection system using soft-attention based convolutional neural networks

Cited by 18 publications

References 42 publications

RETRACTED: Utilizing Generative Adversarial Networks for Acne Dataset Generation in Dermatology

RETRACTED: Utilizing Generative Adversarial Networks for Acne Dataset Generation in Dermatology

Skin Lesion Classification and Detection Using Machine Learning Techniques: A Systematic Review

Dealing imbalance dataset problem in sentiment analysis of recession in Indonesia

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