Deep Learning and Optimization-Based Methods for Skin Lesions Segmentation: A Review

Hosny, Khalid M.; Elshoura, Doaa; Mohamed, Ehab R.; Vrochidou, Eleni; Papakostas, George A.

doi:10.1109/access.2023.3303961

Cited by 18 publications

(5 citation statements)

References 137 publications

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“…Standardized and reproducible outcomes across settings ensure reliability in skin lesion classification. By minimizing subjectivity and variability inherent in traditional approaches, the integration of ACO, WOA, and NN models ensures consistent and reliable classification results, enhancing overall diagnostic reliability and confidence [82].…”

Section: Consistent and Reliable Classificationmentioning

confidence: 99%

Ant Colony and Whale Optimization Algorithms Aided by Neural Networks for Optimum Skin Lesion Diagnosis: A Thorough Review

Mukhlif,

Ramaha,

Hameed

et al. 2024

Mathematics

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The adoption of deep learning (DL) and machine learning (ML) has surged in recent years because of their imperative practicalities in different disciplines. Among these feasible workabilities are the noteworthy contributions of ML and DL, especially ant colony optimization (ACO) and whale optimization algorithm (WOA) ameliorated with neural networks (NNs) to identify specific categories of skin lesion disorders (SLD) precisely, supporting even high-experienced healthcare providers (HCPs) in performing flexible medical diagnoses, since historical patient databases would not necessarily help diagnose other patient situations. Unfortunately, there is a shortage of rich investigations respecting the contributory influences of ACO and WOA in the SLD classification, owing to the recent adoption of ML and DL in the medical field. Accordingly, a comprehensive review is conducted to shed light on relevant ACO and WOA functionalities for enhanced SLD identification. It is hoped, relying on the overview findings, that clinical practitioners and low-experienced or talented HCPs could benefit in categorizing the most proper therapeutical procedures for their patients by referring to a collection of abundant practicalities of those two models in the medical context, particularly (a) time, cost, and effort savings, and (b) upgraded accuracy, reliability, and performance compared with manual medical inspection mechanisms that repeatedly fail to correctly diagnose all patients.

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Section: Consistent and Reliable Classificationmentioning

confidence: 99%

Ant Colony and Whale Optimization Algorithms Aided by Neural Networks for Optimum Skin Lesion Diagnosis: A Thorough Review

Mukhlif,

Ramaha,

Hameed

et al. 2024

Mathematics

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“…In the latter half of 2016, it was also adapted as a Dice loss function [25]. The formula for Dice loss is given in Equation (6).…”

Section: Dice Lossmentioning

confidence: 99%

“…Furthermore, the complexity of the background in CT images consistently provides quite different information when comparing PET and CT scans. As a result of these constraints, deep-learning-based algorithms have proven to be superior in auto-segmenting medical images [6].…”

Section: Introductionmentioning

confidence: 99%

Hyper-Dense_Lung_Seg: Multimodal-Fusion-Based Modified U-Net for Lung Tumour Segmentation Using Multimodality of CT-PET Scans

Alshmrani,

Ni,

Jiang

et al. 2023

Diagnostics

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The majority of cancer-related deaths globally are due to lung cancer, which also has the second-highest mortality rate. The segmentation of lung tumours, treatment evaluation, and tumour stage classification have become significantly more accessible with the advent of PET/CT scans. With the advent of PET/CT scans, it is possible to obtain both functioning and anatomic data during a single examination. However, integrating images from different modalities can indeed be time-consuming for medical professionals and remains a challenging task. This challenge arises from several factors, including differences in image acquisition techniques, image resolutions, and the inherent variations in the spectral and temporal data captured by different imaging modalities. Artificial Intelligence (AI) methodologies have shown potential in the automation of image integration and segmentation. To address these challenges, multimodal fusion approach-based U-Net architecture (early fusion, late fusion, dense fusion, hyper-dense fusion, and hyper-dense VGG16 U-Net) are proposed for lung tumour segmentation. Dice scores of 73% show that hyper-dense VGG16 U-Net is superior to the other four proposed models. The proposed method can potentially aid medical professionals in detecting lung cancer at an early stage.

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“…Following, several studies have highlighted the significant advancements of deep learning algorithms in medical imaging, particularly in the diagnosis and categorization of various diseases, including cancer and skin conditions [9][10][11][12][13][14][15][16]. While many studies focused on diagnosing autoimmune blistering skin diseases using deep neural networks, emphasizing the need for computerized systems to overcome the limitations of current diagnostic methods [9,12], other studies were directed to advanced algorithms for skin lesion segmentation, a critical step in skin cancer diagnosis [10,14,15]. The challenges and recent developments in multiple-lesion recognition, highlighting the complexity of recognizing different lesions simultaneously was explored [11].…”

Section: Introductionmentioning

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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Deep Learning and Optimization-Based Methods for Skin Lesions Segmentation: A Review

Cited by 18 publications

References 137 publications

Ant Colony and Whale Optimization Algorithms Aided by Neural Networks for Optimum Skin Lesion Diagnosis: A Thorough Review

Ant Colony and Whale Optimization Algorithms Aided by Neural Networks for Optimum Skin Lesion Diagnosis: A Thorough Review

Hyper-Dense_Lung_Seg: Multimodal-Fusion-Based Modified U-Net for Lung Tumour Segmentation Using Multimodality of CT-PET Scans

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

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