A Study of CNN and Transfer Learning in Medical Imaging: Advantages, Challenges, Future Scope

Salehi, Ahmad Waleed; Khan, Shakir; Gupta, Gaurav; Alabduallah, Bayan Ibrahimm; Almjally, Abrar; Alsolai, Hadeel; Siddiqui, Tamanna; Mellit, A.

doi:10.3390/su15075930

Cited by 106 publications

(44 citation statements)

References 87 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…A convolutional neural network is a deep learning algorithm that learns image features of relevance to the problem it is designed to solve by the application of a chain of digital lters, the parameters of which are learned. We chose the 48 layer inception v3 CNN architecture 55 pretrained on the ImageNet database because it has been shown to adapt successfully to medical imaging problems through transfer learning with high accuracy 56,57 . The pretrained model already extracts features that enable it to solve image classi cation problems.…”

Section: Methodsmentioning

confidence: 99%

A Machine Learning Method to Process Voice Samples for Identification of Parkinson’s Disease

Iyer

Kemp

Rahmatallah

et al. 2023

Preprint

View full text Add to dashboard Cite

Machine learning approaches have been used to develop methods for the automatic detection of Parkinson’s Disease with voice recordings being the most used data type due to the simple and non-invasive nature of acquiring data. While most studies used voice samples recorded under controlled conditions, a few studies have used voice samples acquired via telephone. Although voice recordings captured via telephone or mobile devices allow much easier and wider access for data collection, current conflicting performance results limit their clinical applicability. The contribution of this study is two-fold: First, we show the reliability of telephone-collected voice recordings of the sustained vowel /a/ by collecting samples from 50 people with Parkinson’s Disease and 50 healthy controls and applying machine learning classification with voice features related to phonation. Second, we utilize a novel application of a convolutional neural network with transfer learning to analyze the spectrograms of the sustained vowel from these samples. This approach considers speech intensity estimates across time and frequency scales rather than collapsing measurements across time., We show the superiority of this pre-trained Inception V3 convolutional neural network model with transfer-learning for the task of classifying people with Parkinson’s Disease as distinct from healthy controls.

show abstract

Section: Methodsmentioning

confidence: 99%

A Machine Learning Method to Process Voice Samples for Identification of Parkinson’s Disease

Iyer

Kemp

Rahmatallah

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…Existing works of DL ASL denoising usually trained a new model on a specific dataset. However, in recent years, pretrained models have shown to be powerful especially in the case where there are not sufficient training data [17], [18], [19]. In general, a pretrained model is developed on a larger dataset with a more general task, which can be then applied to specific downstream tasks with fine-tuning.…”

Section: Pretrained Modelmentioning

confidence: 99%

High Resolution Multi-delay Arterial Spin Labeling with Transformer based Denoising for Pediatric Perfusion MRI

Shou,

Zhao,

Shao

et al. 2024

Preprint

View full text Add to dashboard Cite

Multi-delay arterial spin labeling (MDASL) can quantitatively measure cerebral blood flow (CBF) and arterial transit time (ATT), which is particularly suitable for pediatric perfusion imaging. Here we present a high resolution (iso-2mm) MDASL protocol and performed test-retest scans on 21 typically developing children aged 8 to 17 years. We further proposed a Transformer-based deep learning (DL) model with k-space weighted image average (KWIA) denoised images as reference for training the model. The performance of the model was evaluated by the SNR of perfusion images, as well as the SNR, bias and repeatability of the fitted CBF and ATT maps. The proposed method was compared to several benchmark methods including KWIA, joint denoising and reconstruction with total generalized variation (TGV) regularization, as well as directly applying a pretrained Transformer model on a larger dataset. The results show that the proposed Transformer model with KWIA reference can effectively denoise multi-delay ASL images, not only improving the SNR for perfusion images of each delay, but also improving the SNR for the fitted CBF and ATT maps. The proposed method also improved test-retest repeatability of whole-brain perfusion measurements. This may facilitate the use of MDASL in neurodevelopmental studies to characterize typical and aberrant brain development.

show abstract

“…Convolutional Neural Networks (CNN) is one of the mainstream algorithms powered by DL [46], widely explored in numerous CV application scenarios, such as medicine [47], robotics [48], etc. CNN models are achieved with supervised learning, where a large number of images are presented with labels to the model, then the model, which is composed of a series of layers to detect different features of the input images, will gradually learn the representative features corresponding to each label.…”

Section: Model Architecturementioning

confidence: 99%

Web-based diagnostic platform for microorganism-induced deterioration on paper-based cultural relics with iterative training from human feedback

Liu,

Ben,

Liu

et al. 2024

Preprint

View full text Add to dashboard Cite

Purpose: Paper-based artifacts hold significant cultural and social values. However, paper is intrinsically fragile to microorganisms, such as mold, due to its cellulose composition, which can serve as a microorganisms' nutrient source. Mold not only can damage papers' structural integrity and pose significant challenges to conservation works but also may subject individuals attending the contaminated artifacts to health risks. Current approaches for strain identification usually require extensive training, prolonged time for analysis, expensive operation costs, and higher risks of secondary damage due to sampling. Thus, in current conservation practices with mold-contaminated artifacts, little pre-screening or strain identification was performed before mold removal, and the cleaning techniques are usually broad-spectrum rather than strain-specific. With deep learning showing promising applications across various domains, this study investigated the feasibility of using a convolutional neural network (CNN) for fast in-situ recognition and classification of mold on paper. Methods: Molds were first non-invasively sampled from ancient Xuan Paper-based Chinese books from the Qing and Ming dynasties. Strains were identified using molecular biology methods and the four most prevalent strains were inoculated on Xuan paper to create mockups for image collection. Microscopic images of the molds as well as their stains situated on paper were collected using a compound microscope and commercial microscope lens for cell phone cameras, which were then used for training CNN models with a transfer learning scheme to perform the classification of mold. To enable involvement and contribution from the research community, a web interface that actuates the process while providing interactive features for users to learn about the information of the classified strain was constructed. Moreover, a feedback functionality in the web interface was embedded for catching potential classification errors, adding additional training images, or introducing new strains, all to refine the generalizability and robustness of the model. Results & Conclusion: In the study, we have constructed a suite of high-confidence classification CNN models for the diagnostic process for mold contamination in conservation. At the same time, a web interface was constructed that allows recurrently refining the model with human feedback through engaging the research community. Overall, the proposed framework opens new avenues for effective and timely identification of mold, thus enabling proactive and targeted mold remediation strategies in conservation.

show abstract

A Study of CNN and Transfer Learning in Medical Imaging: Advantages, Challenges, Future Scope

Cited by 106 publications

References 87 publications

A Machine Learning Method to Process Voice Samples for Identification of Parkinson’s Disease

A Machine Learning Method to Process Voice Samples for Identification of Parkinson’s Disease

High Resolution Multi-delay Arterial Spin Labeling with Transformer based Denoising for Pediatric Perfusion MRI

Web-based diagnostic platform for microorganism-induced deterioration on paper-based cultural relics with iterative training from human feedback

Contact Info

Product

Resources

About