Hybrid models for classifying histological images: An association of deep features by transfer learning with ensemble classifier

Oliveira, Cléber I. de; Nascimento, Marcelo Zanchetta do; Roberto, Guilherme Freire; Tosta, Tháına A. A.; Martins, Alessandro Santana; Neves, Leandro Alves

doi:10.1007/s11042-023-16351-4

Cited by 6 publications

(8 citation statements)

References 61 publications

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“…Proposed Method DL + (ReliefF + bGWO) 100 % [29] ResNet50 with fine-tuning, multidimensional and multiscale fractal features 99.39% [15] ResNet50 (activation_48_relu layer), ReliefF and 35 deep-learned features 98.00% [56] 8 CNN models, handcrafted descriptors 97.60% [20] 9 CNN models, handcrafted descriptors 97.50% [62] Le-Net, multidimensional and multiscale fractal features, Haralick and LBPs 91.06% [14] ResNet deep-tuning (DL) 86.67%…”

Section: Author Methods Accuracymentioning

confidence: 99%

“…Proposed Method HFs + (ReliefF + PSO) 100% [17] OralNet: Fused Optimal Deep Features 99.50% [110] Neural architecture search and handcrafted descriptors (morphological and non-morphological) 95.20% [111] Handcrafted descriptors (SIFT, SURF, ORB) 92.80% [100] Handcrafted descriptors (morphological and non-morphological) 92.40% [16] Densenet121 91.91% ResNet50 with fine-tuning, multidimensional and multiscale fractal features 99.62% 99.62% [15] ResNet50 (activation_48_relu layer), ReliefF and 5 deep-learned features -99.32% [57] Inception-V3, Fractal Dimension and Lacunarity (DL+HFs) -99.25% [109] CNN for texture 99.10% 98.20% [112] GIST handcrafted descriptor 88.40% 93.70%…”

Section: Author Methods Accuracymentioning

confidence: 99%

“…These interpretation problems are mainly caused by human issues, such as subjectivity and fatigue. On the other hand, computer-aided diagnosis (CAD) methods play a fundamental role in this task since they can support specialists with second opinions [8][9][10], especially regarding H&E images [5,8,[11][12][13][14][15][16][17].…”

Section: Introductionmentioning

confidence: 99%

“…The second group of descriptors consists of deep-learned (DL) features obtained using convolution neural networks (CNNs) [43]. This group has been useful for defining different CAD approaches [15,17,22,[44][45][46][47][48] that consider data representations with multiple levels of abstraction [8,47,49]. The most explored models have provided the best accuracy rates on the ImageNet dataset [50], such as AlexNet [51], GoogleNet [52], VGGNet [53], ResNet [54], Inception [52] and other applications [20,32,55].…”

Section: Introductionmentioning

confidence: 99%

“…It is essential to observe that EL models can be developed with the most relevant features, exploring a single selection stage to reduce the search space and increase the accuracy of the system [13,15,18]. The use of feature selection through two stages can also be implemented [61][62][63].…”

Section: Introductionmentioning

confidence: 99%

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Ensemble Learning-Based Solutions: An Approach for Evaluating Multiple Features in the Context of H&E Histological Images

Tenguam,

Longo,

Roberto

et al. 2024

Applied Sciences

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In this paper, we propose an approach based on ensemble learning to classify histology tissues stained with hematoxylin and eosin. The proposal was applied to representative images of colorectal cancer, oral epithelial dysplasia, non-Hodgkin’s lymphoma, and liver tissues (the classification of gender and age from liver tissue samples). The ensemble learning considered multiple combinations of techniques that are commonly used to develop computer-aided diagnosis methods in medical imaging. The feature extraction was defined with different descriptors, exploring the deep learning and handcrafted methods. The deep-learned features were obtained using five different convolutional neural network architectures. The handcrafted features were representatives of fractal techniques (multidimensional and multiscale approaches), Haralick descriptors, and local binary patterns. A two-stage feature selection process (ranking with metaheuristics) was defined to obtain the main combinations of descriptors and, consequently, techniques. Each combination was tested through a rigorous ensemble process, exploring heterogeneous classifiers, such as Random Forest, Support Vector Machine, K-Nearest Neighbors, Logistic Regression, and Naive Bayes. The ensemble learning presented here provided accuracy rates from 90.72% to 100.00% and offered relevant information about the combinations of techniques in multiple histological images and the main features present in the top-performing solutions, using smaller sets of descriptors (limited to a maximum of 53), which involved each ensemble process and solutions that have not yet been explored. The developed methodology, i.e., making the knowledge of each ensemble learning comprehensible to specialists, complements the main contributions of this study to supporting the development of computer-aided diagnosis systems for histological images.

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