Breast Fine Needle Cytological Classification Using Deep Hybrid Architectures

Zerouaoui, Hasnae; Idri, Ali; Nakach, Fatima Zahrae; Hadri, Ranya El

doi:10.1007/978-3-030-86960-1_14

Cited by 13 publications

(4 citation statements)

References 35 publications

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“…Intensity normalization (Kociołek et al , 2020) and Contrast Limited Adaptive Histogram Equalization (Yussof et al , 2013) were employed for preprocessing the BreakHis dataset. Similar procedures were used in Zerouaoui et al (2021) and Nakach et al (2022b), with the objective of removing noise, eliminating shadows and improving the contrast of images. As shown in Table I, almost 67 per cent of the histological images in the BreakHis dataset were malignant.…”

Section: Methodsmentioning

confidence: 99%

Binary classification of multi-magnification histopathological breast cancer images using late fusion and transfer learning

Nakach

Zerouaoui²,

Idri³

2023

DTA

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PurposeHistopathology biopsy imaging is currently the gold standard for the diagnosis of breast cancer in clinical practice. Pathologists examine the images at various magnifications to identify the type of tumor because if only one magnification is taken into account, the decision may not be accurate. This study explores the performance of transfer learning and late fusion to construct multi-scale ensembles that fuse different magnification-specific deep learning models for the binary classification of breast tumor slides.Design/methodology/approachThree pretrained deep learning techniques (DenseNet 201, MobileNet v2 and Inception v3) were used to classify breast tumor images over the four magnification factors of the Breast Cancer Histopathological Image Classification dataset (40×, 100×, 200× and 400×). To fuse the predictions of the models trained on different magnification factors, different aggregators were used, including weighted voting and seven meta-classifiers trained on slide predictions using class labels and the probabilities assigned to each class. The best cluster of the outperforming models was chosen using the Scott–Knott statistical test, and the top models were ranked using the Borda count voting system.FindingsThis study recommends the use of transfer learning and late fusion for histopathological breast cancer image classification by constructing multi-magnification ensembles because they perform better than models trained on each magnification separately.Originality/valueThe best multi-scale ensembles outperformed state-of-the-art integrated models and achieved an accuracy mean value of 98.82 per cent, precision of 98.46 per cent, recall of 100 per cent and F1-score of 99.20 per cent.

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Section: Methodsmentioning

confidence: 99%

Binary classification of multi-magnification histopathological breast cancer images using late fusion and transfer learning

Nakach

Zerouaoui²,

Idri³

2023

DTA

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“…Another study presented a meta-analysis [10], which affirmed the potential of neural network algorithms in aiding FNAC cancer diagnosis. At the same time [11] explored twenty-eight hybrid architectures combining various deep learning techniques for feature extraction (DenseNet 201, Inception V3, Inception ReseNet V2, MobileNet V2, ResNet 50, VGG16, and VGG19) with classifiers (multilayer perceptron (MLP), support vector machine (SVM), decision tree (DT), and K-nearest neighbor (KNN)) to advance binary classification of breast cytological images. Unfortunately, the dataset is wrongly cited, and they worked with the same dataset as in [5].…”

Section: Earlier Workmentioning

confidence: 99%

A new deep learning model with interface for fine needle aspiration cytology image-based breast cancer detection

Kalita,

Mahanta,

Das

et al. 2024

IJEECS

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Cytological evaluation through microscopic image analysis of fine needle aspiration cytology (FNAC) is pivotal in the initial screening of breast cancer. The sensitivity of FNAC as a screening tool relies on both image quality and the pathologist’s expertise. To enhance diagnostic accuracy and alleviate the pathologist’s workload, a computer-aided diagnosis (CAD) system was developed. A comparative study was conducted, assessing twelve candidate pre-trained models. Utilizing a locally gathered FNAC image dataset, three superior models-MobileNet-V2, DenseNet-121, and Inception-V3-were selected based on their training, validation, and testing accuracies. Further, these models underwent evaluation in four transfer learning scenarios to enhance testing accuracy. While the outcomes were promising, they left room for improvement, motivating us to create a novel deep convolutional neural network (CNN). The newly proposed model exhibited robust performance with testing accuracy at 85%. Our research concludes that the most lightweight, high-accuracy model is the one we propose. We’ve integrated it into our user-friendly Android App, “Breast Cancer Detection System,” in TensorFlow Lite format, with cloud database support, showcasing its effectiveness. Implementing an artificial intelligent (AI)-based diagnosis system with a user-friendly interface holds the potential to enhance early breast cancer detection using FNAC.

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“…This section presents the data preparation process applied on the histological BreakHis dataset, which consists of data acquisition, data preprocessing using intensity normalization and Contrast Limited Adaptive Histogram Equalization (CLAHE) and data augmentation. The same process for the data preparation is followed in the studies (Zerouaoui et al , 2021; Zerouaoui and Idri, 2022), and therefore it will be summarized in this subsection.…”

Section: Data Preparationmentioning

confidence: 99%

“…In order to compare the results of the study (El Alaoui et al , 2022), and to elevate the burdens of the previous related works, this paper develops and evaluates the performances of 24 deep hybrid heterogenous ensembles (DHHtEs) using DL models (DenseNet 201 (Huang et al , 2017), Inception V3, VGG16, VGG19, Inception-ResNet-V3, MobileNet V2 and ResNet 50) for FE and ML models (MLP, SVM, DT and KNN) for classification over the BreakHis histological images dataset. The choice of the members of base learners for the DHHtEs is based on the finding of the previous studies (Zerouaoui et al , 2021; Zerouaoui and Idri, 2022) which designed 28 hybrid architectures using seven DL techniques for FE including DenseNet 201, Inception V3, VGG16, VGG19, Inception-ResNet-V3, MobileNet V2 and ResNet 50 and four ML classifiers (MLP, SVM, DT and KNN). Results showed that for all the four MF values 40×, 100×, 200× and 400× of the BreakHis dataset, the hybrid architecture MLP for classification and DenseNet 201 for feature extraction (MDEN) constructed using DenseNet 201 for FE and MLP for classification outperformed the others.…”

Section: Introductionmentioning

confidence: 99%

A new approach for histological classification of breast cancer using deep hybrid heterogenous ensemble

Zerouaoui

Idri

Alaoui³

2022

DTA

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PurposeHundreds of thousands of deaths each year in the world are caused by breast cancer (BC). An early-stage diagnosis of this disease can positively reduce the morbidity and mortality rate by helping to select the most appropriate treatment options, especially by using histological BC images for the diagnosis.Design/methodology/approachThe present study proposes and evaluates a novel approach which consists of 24 deep hybrid heterogenous ensembles that combine the strength of seven deep learning techniques (DenseNet 201, Inception V3, VGG16, VGG19, Inception-ResNet-V3, MobileNet V2 and ResNet 50) for feature extraction and four well-known classifiers (multi-layer perceptron, support vector machines, K-nearest neighbors and decision tree) by means of hard and weighted voting combination methods for histological classification of BC medical image. Furthermore, the best deep hybrid heterogenous ensembles were compared to the deep stacked ensembles to determine the best strategy to design the deep ensemble methods. The empirical evaluations used four classification performance criteria (accuracy, sensitivity, precision and F1-score), fivefold cross-validation, Scott–Knott (SK) statistical test and Borda count voting method. All empirical evaluations were assessed using four performance measures, including accuracy, precision, recall and F1-score, and were over the histological BreakHis public dataset with four magnification factors (40×, 100×, 200× and 400×). SK statistical test and Borda count were also used to cluster the designed techniques and rank the techniques belonging to the best SK cluster, respectively.FindingsResults showed that the deep hybrid heterogenous ensembles outperformed both their singles and the deep stacked ensembles and reached the accuracy values of 96.3, 95.6, 96.3 and 94 per cent across the four magnification factors 40×, 100×, 200× and 400×, respectively.Originality/valueThe proposed deep hybrid heterogenous ensembles can be applied for the BC diagnosis to assist pathologists in reducing the missed diagnoses and proposing adequate treatments for the patients.

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Breast Fine Needle Cytological Classification Using Deep Hybrid Architectures

Cited by 13 publications

References 35 publications

Binary classification of multi-magnification histopathological breast cancer images using late fusion and transfer learning

Binary classification of multi-magnification histopathological breast cancer images using late fusion and transfer learning

A new deep learning model with interface for fine needle aspiration cytology image-based breast cancer detection

A new approach for histological classification of breast cancer using deep hybrid heterogenous ensemble

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