Ensemble the recent architectures of deep convolutional networks for skin diseases diagnosis

Duman, Erkan; Tolan, Zafer

doi:10.1002/ima.22872

Cited by 8 publications

(3 citation statements)

References 39 publications

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“…Yadav, et al [61] implemented a sailfish-based gradient boosting framework for recognizing skin lesions, focusing on high segmentation and classification accuracy. Duman and Tolan [62] combined CNN models in an ensemble approach for diagnosing skin diseases from color lesion images. Kalaivani and Karpagavalli [63] developed a fuzzy transfer learning model as a domain adaptation technique, combined with a bootstrapping of fine-tuned segmentation and classification.…”

Section: Innovative Approaches and Combination Strategiesmentioning

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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Section: Innovative Approaches and Combination Strategiesmentioning

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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“…The evaluation of the severity of disorders is contingent upon the utilization of appropriate diagnostic procedures. Table 1 presents a diagnostic approach as demonstrated by Duman and Tolan [1]. Hussain, Siersbaek, and Østerdal [2] present a comprehensive breakdown of the five distinct categories comprising an individual's health data in Table 2.…”

Section: Fig 1 Big Data Analytics Processmentioning

confidence: 99%

Interventions for Disrupted Sleep Patterns and Insomnia in Pediatric and Adolescent Populations Diagnosed with ASD

Beyers

2024

JBSHA

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The objective of this study is to develop a set of recommendations for effectively addressing sleep disturbance in adolescents and children with autism spectrum disorder (ASD). This will be achieved through a comprehensive evaluation of both pharmacological and non-pharmacological approaches utilised in the treatment of sleep disruptions in this specific population. Healthcare professionals who are providing care for adolescents and children with ASD and experiencing sleep disturbances should actively investigate potential underlying factors contributing to the disruption, such as medication usage or the presence of comorbid conditions. Subsequently, it is crucial for clinicians to address these identified causes in order to effectively manage the sleep disturbances. When managing sleep disturbance, it is advisable for clinicians to first suggest behavioural interventions, either independently or in combination with pharmacological or nutraceutical treatments, in order to facilitate improved sleep for children and their families. In cases where behavioural interventions prove ineffective and comorbid conditions and concurrent drug usage have been appropriately managed, healthcare professionals may contemplate the administration of melatonin, commencing with a conservative dosage. It is advisable to recommend melatonin of pharmaceutical grade if it is readily available for clinical use. It is incumbent upon healthcare professionals to engage in comprehensive discussions regarding the potential advantages and drawbacks associated with the utilisation of melatonin among paediatric patients, adolescents, and their guardians.

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“… 6 Compared with traditional methods, methods based on convolutional neural networks (CNN) can learn meaningful features directly from data, Esteva et al 7 employed a CNN framework based on Inception-V3 to train a skin disease classification model with an accuracy rate of 71.2% and verified that the algorithm was capable of categorization accuracy on par with 21 dermatologists who hold board certification. Duman et al 8 propose a novel ensemble method that combines various advantages of several existing CNN models to deal with large-scale imbalanced datasets. By using the weighted aggregation method, the accuracy score is improved by 5% to 10%, compared with the state-of-the-art, the average sensitivity and area under the curve (AUC) values are 0.825 and 0.923, respectively, ranking second.…”

Section: Introductionmentioning

confidence: 99%

HI-MViT: A lightweight model for explainable skin disease classification based on modified MobileViT

Ding,

Yi,

et al. 2023

DIGITAL HEALTH

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Objective To develop an explainable lightweight skin disease high-precision classification model that can be deployed to the mobile terminal. Methods In this study, we present HI-MViT, a lightweight network for explainable skin disease classification based on Modified MobileViT. HI-MViT is mainly composed of ordinary convolution, Improved-MV2, MobileViT block, global pooling, and fully connected layers. Improved-MV2 uses the combination of shortcut and depth classifiable convolution to substantially decrease the amount of computation while ensuring the efficient implementation of information interaction and memory. The MobileViT block can efficiently encode local and global information. In addition, semantic feature dimensionality reduction visualization and class activation mapping visualization methods are used for HI-MViT to further understand the attention area of the model when learning skin lesion images. Results The International Skin Imaging Collaboration has assembled and made available the ISIC series dataset. Experiments using the HI-MViT model on the ISIC-2018 dataset achieved scores of 0.931, 0.932, 0.961, and 0.977 on F1-Score, Accuracy, Average Precision (AP), and area under the curve (AUC). Compared with the top five algorithms of ISIC-2018 Task 3, Marco's average F1-Score, AP, and AUC have increased by 6.9%, 6.8%, and 0.8% compared with the suboptimal performance model. Compared with ConvNeXt, the most competitive convolutional neural network architecture, our model is 5.0%, 3.4%, 2.3%, and 2.2% higher in F1-Score, Accuracy, AP, and AUC, respectively. The experiments on the ISIC-2017 dataset also achieved excellent results, and all indicators were better than the top five algorithms of ISIC-2017 Task 3. Using the trained model to test on the PH2 dataset, an excellent performance score is obtained, which shows that it has good generalization performance. Conclusions The skin disease classification model HI-MViT proposed in this article shows excellent classification performance and generalization performance in experiments. It demonstrates how the classification outcomes can be applied to dermatologists’ computer-assisted diagnostics, enabling medical professionals to classify various dermoscopic images more rapidly and reliably.

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Ensemble the recent architectures of deep convolutional networks for skin diseases diagnosis

Cited by 8 publications

References 39 publications

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

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

Interventions for Disrupted Sleep Patterns and Insomnia in Pediatric and Adolescent Populations Diagnosed with ASD

HI-MViT: A lightweight model for explainable skin disease classification based on modified MobileViT

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