Comparison of Transfer Learning and Conventional Machine Learning Applied to Structural Brain MRI for the Early Diagnosis and Prognosis of Alzheimer's Disease

Nanni, Loris; Interlenghi, Matteo; Brahnam, Sheryl; Salvatore, Christian; Papa, Sergio; Nemni, Raffaello; Castiglioni, Isabella

doi:10.3389/fneur.2020.576194

Cited by 53 publications

(28 citation statements)

References 72 publications

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“…Since the network's weights are meaningful to solve a related task, they could be used as initialization for the task of interest, updating the weights using a smaller dataset. For example, Nanni et al, 2020 used pretrained networks (trained on the ImageNet database [69]) to classify pictures of everyday objects (number of pictures in the order of millions), for prognostic purposes in Alzheimer's disease (number of MR images in the order of hundreds) [70].…”

Section: Solutions To Scarcity Of Longitudinal Datamentioning

confidence: 99%

Towards Multimodal Machine Learning Prediction of Individual Cognitive Evolution in Multiple Sclerosis

Denissen

Chén

Mey

et al. 2021

JPM

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Multiple sclerosis (MS) manifests heterogeneously among persons suffering from it, making its disease course highly challenging to predict. At present, prognosis mostly relies on biomarkers that are unable to predict disease course on an individual level. Machine learning is a promising technique, both in terms of its ability to combine multimodal data and through the capability of making personalized predictions. However, most investigations on machine learning for prognosis in MS were geared towards predicting physical deterioration, while cognitive deterioration, although prevalent and burdensome, remained largely overlooked. This review aims to boost the field of machine learning for cognitive prognosis in MS by means of an introduction to machine learning and its pitfalls, an overview of important elements for study design, and an overview of the current literature on cognitive prognosis in MS using machine learning. Furthermore, the review discusses new trends in the field of machine learning that might be adopted for future studies in the field.

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Section: Solutions To Scarcity Of Longitudinal Datamentioning

confidence: 99%

Towards Multimodal Machine Learning Prediction of Individual Cognitive Evolution in Multiple Sclerosis

Denissen

Chén

Mey

et al. 2021

JPM

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“…Transfer Learning-based model training: Transfer learning (TL) is a technique where an existing pre-trained model is reused for a new classification task [ 52 ]. TL has been proven to achieve good performance on many image classification tasks [ 53 , 54 , 55 , 56 ]. Visually, this process is illustrated in Figure 14 , where the pre-trained models are trained on large datasets such as ResNet101 and DenseNet201 in this work.…”

Section: Methodsmentioning

confidence: 99%

Skin Lesion Segmentation and Multiclass Classification Using Deep Learning Features and Improved Moth Flame Optimization

et al. 2021

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Manual diagnosis of skin cancer is time-consuming and expensive; therefore, it is essential to develop automated diagnostics methods with the ability to classify multiclass skin lesions with greater accuracy. We propose a fully automated approach for multiclass skin lesion segmentation and classification by using the most discriminant deep features. First, the input images are initially enhanced using local color-controlled histogram intensity values (LCcHIV). Next, saliency is estimated using a novel Deep Saliency Segmentation method, which uses a custom convolutional neural network (CNN) of ten layers. The generated heat map is converted into a binary image using a thresholding function. Next, the segmented color lesion images are used for feature extraction by a deep pre-trained CNN model. To avoid the curse of dimensionality, we implement an improved moth flame optimization (IMFO) algorithm to select the most discriminant features. The resultant features are fused using a multiset maximum correlation analysis (MMCA) and classified using the Kernel Extreme Learning Machine (KELM) classifier. The segmentation performance of the proposed methodology is analyzed on ISBI 2016, ISBI 2017, ISIC 2018, and PH2 datasets, achieving an accuracy of 95.38%, 95.79%, 92.69%, and 98.70%, respectively. The classification performance is evaluated on the HAM10000 dataset and achieved an accuracy of 90.67%. To prove the effectiveness of the proposed methods, we present a comparison with the state-of-the-art techniques.

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“…We identified four studies which used transfer learning for classification (Jain et al 2019;Lu et al 2018;W. Li et al 2020;Nanni et al 2020). Transfer learning was typically used for fine tuning neural networks, particularly when the authors felt the dataset was not sufficiently large enough to properly train the neural network algorithm.…”

Section: Ai Methodsmentioning

confidence: 99%

“…Contributing to heterogeneity, the aim of "diagnosis" differed between studies using structural MRI. For example, there were 17 studies specifically targeting early diagnosis in which "early" disease was variably defined by: MMSE score < 24 (Cheng et al 2017;Coupé et al 2015;Gorji and Haddadnia 2015); CDR 0.5-1 (Dai et al 2012;Hojjati, Ebrahimzadeh, and Babajani-Feremi 2019;Khedher et al 2015;Nanni et al 2020)…”

Section: Diagnosis Using Volumetric Structural Mrimentioning

confidence: 99%

Artificial intelligence for diagnosis and prognosis in neuroimaging for dementia; a systematic review

Borchert

Azevedo

Badhwar

et al. 2021

Preprint

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Introduction Recent developments in artificial intelligence (AI) and neuroimaging offer new opportunities for improving diagnosis and prognosis of dementia. To synthesise the available literature, we performed a systematic review. Methods We systematically reviewed primary research publications up to January 2021, using AI for neuroimaging to predict diagnosis and/or prognosis in cognitive neurodegenerative diseases. After initial screening, data from each study was extracted, including: demographic information, AI methods, neuroimaging features, and results. Results We found 2709 reports, with 252 eligible papers remaining following screening. Most studies relied on the Alzheimers Disease Neuroimaging Initiative (ADNI) dataset (n=178) with no other individual dataset used more than 5 times. Algorithmic classifiers, such as support vector machine (SVM), were the most commonly used AI method (47%) followed by discriminative (32%) and generative (11%) classifiers. Structural MRI was used in 71% of studies with a wide range of accuracies for the diagnosis of neurodegenerative diseases and predicting prognosis. Lower accuracy was found in studies using a multi-class classifier or an external cohort as the validation group. There was improvement in accuracy when neuroimaging modalities were combined, e.g. PET and structural MRI. Only 17 papers studied non-Alzheimers disease dementias. Conclusion The use of AI with neuroimaging for diagnosis and prognosis in dementia is a rapidly emerging field. We make a number of recommendations addressing the definition of key clinical questions, heterogeneity of AI methods, and the availability of appropriate and representative data. We anticipate that addressing these issues will enable the field to move towards meaningful clinical translation.

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Comparison of Transfer Learning and Conventional Machine Learning Applied to Structural Brain MRI for the Early Diagnosis and Prognosis of Alzheimer's Disease

Cited by 53 publications

References 72 publications

Towards Multimodal Machine Learning Prediction of Individual Cognitive Evolution in Multiple Sclerosis

Towards Multimodal Machine Learning Prediction of Individual Cognitive Evolution in Multiple Sclerosis

Skin Lesion Segmentation and Multiclass Classification Using Deep Learning Features and Improved Moth Flame Optimization

Artificial intelligence for diagnosis and prognosis in neuroimaging for dementia; a systematic review

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