Please use this identifier to cite or link to this item: http://hdl.handle.net/10553/119849
Title: Performance Evaluation of Deep Learning Models for Image Classification Over Small Datasets: Diabetic Foot Case Study
Authors: Hernández Guedes, Abián 
Santana Pérez, Idafen 
Arteaga Marrero,Natalia 
Fabelo Gómez, Himar Antonio 
Marrero Callicó, Gustavo Iván 
Ruiz Alzola, Juan Bautista 
Keywords: Deep learning
Information theory
Information bottleneck
Diabetes
Thermal imaging
Issue Date: 2022
Project: Talent Imágenes Hiperespectrales Para Aplicaciones de Inteligencia Artificial 
Journal: IEEE Access 
Abstract: Data scarcity is a common and challenging issue when working with Artificial Intelligence solutions, especially those including Deep Learning (DL) models for tasks such as image classification. This is particularly relevant in healthcare scenarios, in which data collection requires a long-lasting process, involving specific control protocols. The performance of DL models is usually quantified by different classification metrics, which may provide biased results, due to the lack of sufficient data. In this paper, an innovative approach is proposed to evaluate the performance of DL models when labeled data is scarce. This approach, which aims to detect the poor performance provided by DL models, in spite of traditional assessing metrics indicating otherwise, is based on information theoretic concepts and motivated by the Information Bottleneck framework. This methodology has been evaluated by implementing several experimental configurations to classify samples from a plantar thermogram dataset, focused on early stage detection of diabetic foot ulcers, as a case study. The proposed network architectures exhibited high results in terms of classification metrics. However, as our approach shows, only two of those models are indeed consistent to generalize the data properly. In conclusion, a new methodology was introduced and tested to identify promising DL models for image classification over small datasets without relying exclusively on the widely employed classification metrics. Example code and supplementary material using a state-of-the-art DL model are available at https://github.com/mt4sd/PerformanceEvaluationScarceDataset
URI: http://hdl.handle.net/10553/119849
ISSN: 2169-3536
DOI: 10.1109/ACCESS.2022.3225107
Source: IEEE Access [ISSN 2169-3536], v. 10, p. 124373-124386, (Septiembre 2022)
Appears in Collections:Artículos
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