<scp>MacD‐Net</scp>: An automatic guided‐ensemble approach for macular pathology detection using optical coherence tomography images

Maurya, Ritesh; Pandey, Nageshwar Nath; Joshi, Rakesh Chandra; Dutta, Malay Kishore

doi:10.1002/ima.22954

Cited by 4 publications

(1 citation statement)

References 48 publications

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“…In [ 60 ], the hybrid ensemble deep network (HEDN) model based on the MobileNet-v2, ResNet50, and VGG16 models is presented to classify OCT retinal pathology images into four classes. Furthermore, Maurya et al in [ 61 ] managed to reach 99.8% accuracy on the test data of the UCSD dataset by fine-tuning the MobileNet-v1, EfficientNet-B3, network architecture search network for mobiles (NASNetMobile) and Xception models and assembling them. In [ 62 ], a speckle reduction filter was applied to OCT images.…”

Section: Related Workmentioning

confidence: 99%

Stitched vision transformer for age-related macular degeneration detection using retinal optical coherence tomography images

Azizi,

Abhari,

Sajedi

2024

PLoS ONE

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Age-related macular degeneration (AMD) is an eye disease that leads to the deterioration of the central vision area of the eye and can gradually result in vision loss in elderly individuals. Early identification of this disease can significantly impact patient treatment outcomes. Furthermore, given the increasing elderly population globally, the importance of automated methods for rapidly monitoring at-risk individuals and accurately diagnosing AMD is growing daily. One standard method for diagnosing AMD is using optical coherence tomography (OCT) images as a non-invasive imaging technology. In recent years, numerous deep neural networks have been proposed for the classification of OCT images. Utilizing pre-trained neural networks can speed up model deployment in related tasks without compromising accuracy. However, most previous methods overlook the feasibility of leveraging pre-existing trained networks to search for an optimal architecture for AMD staging on a new target dataset. In this study, our objective was to achieve an optimal architecture in the efficiency-accuracy trade-off for classifying retinal OCT images. To this end, we employed pre-trained medical vision transformer (MedViT) models. MedViT combines convolutional and transformer neural networks, explicitly designed for medical image classification. Our approach involved pre-training two distinct MedViT models on a source dataset with labels identical to those in the target dataset. This pre-training was conducted in a supervised manner. Subsequently, we evaluated the performance of the pre-trained MedViT models for classifying retinal OCT images from the target Noor Eye Hospital (NEH) dataset into the normal, drusen, and choroidal neovascularization (CNV) classes in zero-shot settings and through five-fold cross-validation. Then, we proposed a stitching approach to search for an optimal model from two MedViT family models. The proposed stitching method is an efficient architecture search algorithm known as stitchable neural networks. Stitchable neural networks create a candidate model in search space for each pair of stitchable layers by inserting a linear layer between them. A pair of stitchable layers consists of layers, each selected from one input model. While stitchable neural networks had previously been tested on more extensive and general datasets, this study demonstrated that stitching networks could also be helpful in smaller medical datasets. The results of this approach indicate that when pre-trained models were available for OCT images from another dataset, it was possible to achieve a model in 100 epochs with an accuracy of over 94.9% in classifying images from the NEH dataset. The results of this study demonstrate the efficacy of stitchable neural networks as a fine-tuning method for OCT image classification. This approach not only leads to higher accuracy but also considers architecture optimization at a reasonable computational cost.

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Section: Related Workmentioning

confidence: 99%

Stitched vision transformer for age-related macular degeneration detection using retinal optical coherence tomography images

Azizi,

Abhari,

Sajedi

2024

PLoS ONE

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Artificial intelligence for diagnosing exudative age-related macular degeneration

Kang,

Lo,

Zhang

et al. 2024

Cochrane Database of Systematic Reviews

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Skin cancer detection through attention guided dual autoencoder approach with extreme learning machine

Maurya,

Mahapatra,

Dutta

et al. 2024

Sci Rep

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Skin cancer is a lethal disease, and its early detection plays a pivotal role in preventing its spread to other body organs and tissues. Artificial Intelligence (AI)-based automated methods can play a significant role in its early detection. This study presents an AI-based novel approach, termed 'DualAutoELM' for the effective identification of various types of skin cancers. The proposed method leverages a network of autoencoders, comprising two distinct autoencoders: the spatial autoencoder and the FFT (Fast Fourier Transform)-autoencoder. The spatial-autoencoder specializes in learning spatial features within input lesion images whereas the FFT-autoencoder learns to capture textural and distinguishing frequency patterns within transformed input skin lesion images through the reconstruction process. The use of attention modules at various levels within the encoder part of these autoencoders significantly improves their discriminative feature learning capabilities. An Extreme Learning Machine (ELM) with a single layer of feedforward is trained to classify skin malignancies using the characteristics that were recovered from the bottleneck layers of these autoencoders. The 'HAM10000' and 'ISIC-2017' are two publicly available datasets used to thoroughly assess the suggested approach. The experimental findings demonstrate the accuracy and robustness of the proposed technique, with AUC, precision, and accuracy values for the 'HAM10000' dataset being 0.98, 97.68% and 97.66%, and for the 'ISIC-2017' dataset being 0.95, 86.75% and 86.68%, respectively. This study highlights the possibility of the suggested approach for accurate detection of skin cancer.

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MacD‐Net: An automatic guided‐ensemble approach for macular pathology detection using optical coherence tomography images

Cited by 4 publications

References 48 publications

Stitched vision transformer for age-related macular degeneration detection using retinal optical coherence tomography images

Stitched vision transformer for age-related macular degeneration detection using retinal optical coherence tomography images

Artificial intelligence for diagnosing exudative age-related macular degeneration

Skin cancer detection through attention guided dual autoencoder approach with extreme learning machine

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