Unraveling the complexity of Optical Coherence Tomography image segmentation using machine and deep learning techniques: A review

Nawaz, Mehmood; Uvaliyev, Adilet; Bibi, Khadija; Wei, Hao; Abaxi, Sai Mu Dalike; Masood, Anum; Shi, Peilun; Ho, Ho-Pui; Yuan, Wu

doi:10.1016/j.compmedimag.2023.102269

Cited by 19 publications

(4 citation statements)

References 93 publications

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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%

“…The use of deep learning in detecting various types of cancer, underlining the role of these technologies in early diagnosis and improved patient outcomes was investigated [13,14]. Segmentation of optical coherence tomography images, a challenging task crucial for diagnosing diseases like glaucoma was explored [15]. Lastly, a study surveyed deep learning in human cancer categorization, emphasizing the effectiveness of convolutional neural networks (CNN) in classifying histopathological images [16].…”

Section: Introductionmentioning

confidence: 99%

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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: Introductionmentioning

confidence: 99%

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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“…Similarly, in cardiovascular medicine, AI methods have been used to analyze CT angiography images to identify blockages and other abnormalities in blood vessels [5]. However, while the potential of AI in CT image analysis is immense, there are challenges and considerations reported by several researchers [8][9][10][11]. One major challenge is the need for large, diverse datasets to train the AI algorithms, ensuring they are robust and capable of generalizing across different populations.…”

Section: Introductionmentioning

confidence: 99%

Deciphering the Efficacy of No-Attention Architectures in Computed Tomography Image Classification: A Paradigm Shift

Alzahrani

2024

Mathematics

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The burgeoning domain of medical imaging has witnessed a paradigm shift with the integration of AI, particularly deep learning, enhancing diagnostic precision and expediting the analysis of Computed Tomography (CT) images. This study introduces an innovative Multilayer Perceptron-driven model, DiagnosticMLP, which sidesteps the computational intensity of attention-based mechanisms, favoring a no-attention architecture that leverages Fourier Transforms for global information capture and spatial gating units for local feature emphasis. This study’s methodology encompasses a sophisticated augmentation and patching strategy at the input level, followed by a series of MLP blocks designed to extract hierarchical features and spatial relationships, culminating in a global average pooling layer before classification. Evaluated against state-of-the-art MLP-based models including MLP-Mixer, FNet, gMLP, and ResMLP across diverse and extensive CT datasets, including abdominal, and chest scans, DiagnosticMLP demonstrated a remarkable ability to converge efficiently, with competitive accuracy, F1 scores, and AUC metrics. Notably, in datasets featuring kidney and abdomen disorders, the model showcased superior generalization capabilities, underpinned by its unique design that addresses the complexity inherent in CT imaging. The findings in terms of accuracy and precision-recall balance posit DiagnosticMLP as an exceptional outperforming alternative to attention-reliant models, paving the way for streamlined, efficient, and scalable AI tools in medical diagnostics, reinforcing the potential for AI-augmented precision medicine without the dependency on attention-based architectures.

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“…Segmentation is an important process needed in the image recognition process for the process of extracting features that we will take as data in a study. Several image segmentation methods can be used in the feature retrieval process [4]- [6]. Taking an image is carried out to obtain the information contained in it with image segmentation techniques used to extract the information contained in the image.…”

Section: Introductionmentioning

confidence: 99%

Comparison of the suitability of the otsu method thresholding and multilevel thresholding for flower image segmentation

Hadiq,

Solehatin,

Djuniharto

et al. 2023

J. Soft Comput. Explor.

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The digital representation of flowers, characterized by their vivid chromatic attributes, establishes them as viable candidates for deployment as input imagery within the object recognition paradigm. Within the context of object recognition, the imperative of a proficient image segmentation process is underscored, serving to effectively discern the object from its background and, consequently, optimizing the efficacy of the object recognition process. This research unfolds through a methodologically structured tripartite framework, encompassing the initial stage involving input imagery, the subsequent intermediate phase dedicated to image segmentation, and a conclusive stage centered on the quantitative evaluation of methodological outcomes. The second stage, focusing on image segmentation, employs the Otsu thresholding and multilevel thresholding methods. The subsequent third stage involves a thorough assessment of segmentation outcomes through the application of quantitative metrics, including Peak signal-to-oise ratio (PSNR) and Root Mean Square Error (RMSE). Empirical investigations, incorporating a diverse array of floral input images, reveal a conspicuous inclination towards a specific segmentation methodology. Specifically, the Otsu Thresholding method emerges as the more judicious choice relative to multilevel Thresholding, demonstrating superior performance with a diminished RMSE value and an augmented PSNR value, substantiated by an average RMSE value. This research is propelled by the overarching objective of discerning the most optimal method for the segmentation of flower images, particularly in the face of diverse input images. Its significant contribution lies in providing nuanced insights into the discerning selection of segmentation methodologies, attuned to the variability inherent in diverse forms of input imagery, thereby culminating in optimized outcomes within the domain of flower image recognition. Where did these results come from? please show it in the sub-discussion.

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Unraveling the complexity of Optical Coherence Tomography image segmentation using machine and deep learning techniques: A review

Cited by 19 publications

References 93 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

Deciphering the Efficacy of No-Attention Architectures in Computed Tomography Image Classification: A Paradigm Shift

Comparison of the suitability of the otsu method thresholding and multilevel thresholding for flower image segmentation

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