Conventional Machine Learning and Deep Learning Approach for Multi-Classification of Breast Cancer Histopathology Images—a Comparative Insight

Sharma, Shallu; Mehra, Rajesh

doi:10.1007/s10278-019-00307-y

Cited by 192 publications

(74 citation statements)

References 49 publications

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“…This comes from a large number of images for ductal carcinoma in the BreakHis dataset and the similarity of malignant sample tissues. Our proposed models are solving the difficulty of classification in Fibro Adenoma (FA) and Mucous Carcinoma (MC) classes, which study [64] has mentioned. The misclassified images are mostly benign samples that are predicted as malignant.…”

Section: Evaluation Of Resultsmentioning

confidence: 99%

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Deep Learning Applied for Histological Diagnosis of Breast Cancer

Yari

Nguyen

2020

IEEE Access

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Deep learning, as one of the currently most popular computer science research trends, improves neural networks, which has more and deeper layers allowing higher abstraction levels and more accurate data-based. Although deep convolutional neural networks, as a deep learning algorithm, has recently achieved promising results in data analysis, the requirement for a large amount of data prevents its use in medical data analysis since it is challenging to obtain data from the medical field. Breast cancer is a common cancer in women. To diagnose this kind of cancer, breast cell shapes in histopathology images should be examined by senior pathologists. The number of pathologists per population in the world is not enough, especially in Africa, and human mistake may occur in diagnosis procedure. After the evaluation of deep learning methods and algorithms in breast histological data processing, we tried to improve the current systems' accuracy. As a result, this study proposes two effective deep transfer learning-based models, which rely on pre-trained DCNN using a large collection of ImageNet dataset images that improve current state-of-the-art systems in both binary and multiclass classification. We transfer pre-trained weights of the ResNet50 and DesneNet121 on the Imagenet as initial weights and fine-tune these models with a deep classifier with data augmentation to detect various malignant and benign samples tissues in the two categories of binary classification and multiclass classification. The proposed models have been examined with optimized hyperparameters in magnification-dependent and magnification-independent classification modes. In the multiclass classification, the proposed system achieved up to 98% accuracy. As for binary classification, the proposed system provides up to 100% accuracy. The results outperform previous studies accuracies in all defined performance metrics in breast cancer CAD systems from histological images.

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Section: Evaluation Of Resultsmentioning

confidence: 99%

“…In [64], two methods for the classification of histology images have been proposed. The first method is reliant on handcrafted features in which Hu moment, color histogram, and Haralick texture are used for the extraction of features from images of the BreakHis dataset.…”

Section: Multiclass Classificationmentioning

confidence: 99%

Deep Learning Applied for Histological Diagnosis of Breast Cancer

Yari

Nguyen

2020

IEEE Access

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“…Various CNNs are available for the classification of images. Commonly used CNNs for histological and cytological images are VGG16 [ 16 , 26 , 27 ], InceptionV3 [ 28 , 29 ], and InceptionResNetV2 [ 30 ]. Some of these CNNs are rather large (VGG16, InceptionResNetV2) and achieve high accuracies with large training datasets.…”

Section: Discussionmentioning

confidence: 99%

Identification of Gastritis Subtypes by Convolutional Neuronal Networks on Histological Images of Antrum and Corpus Biopsies

Steinbuß

Kriegsmann

2020

IJMS

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Background: Gastritis is a prevalent disease and commonly classified into autoimmune (A), bacterial (B), and chemical (C) type gastritis. While the former two subtypes are associated with an increased risk of developing gastric intestinal adenocarcinoma, the latter subtype is not. In this study, we evaluated the capability to classify common gastritis subtypes using convolutional neuronal networks on a small dataset of antrum and corpus biopsies. Methods: 1230 representative 500 × 500 µm images of 135 patients with type A, type B, and type C gastritis were extracted from scanned histological slides. Patients were allocated randomly into a training set (60%), a validation set (20%), and a test set (20%). One classifier for antrum and one classifier for corpus were trained and optimized. After optimization, the test set was analyzed using a joint result from both classifiers. Results: Overall accuracy in the test set was 84% and was particularly high for type B gastritis with a sensitivity of 100% and a specificity of 93%. Conclusions: Classification of gastritis subtypes is possible using convolutional neural networks on a small dataset of histopathological images of antrum and corpus biopsies. Deep learning strategies to support routine diagnostic pathology merit further evaluation.

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“…Through previous literature and preliminary experiments, the VGG16 model demonstrated a balance in framework, accuracy, computational efficiency and proven performance in the medical field, and was chosen for our experiments ( 19 – 21 ). The model comprises 16 layers, including 13 convolutional layers with 3×3 convolution kernels and 3 fully connected layers.…”

Section: Methodsmentioning

confidence: 99%

Artificial classification of cervical squamous lesions in ThinPrep cytologic tests using a deep convolutional neural network

Liu

Wang

et al. 2020

Oncol Lett

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The diagnosis of squamous cell carcinoma requires the accurate classification of cervical squamous lesions in the ThinPrep cytologic test (TCT). It primarily relies on a pathologist's interpretation under a microscope. Deep convolutional neural networks (DCNN) have played an increasingly important role in digital pathology. However, they have not been applied to diverse datasets and externally validated. In the present study, a DCNN model based on VGG16 and an ensemble training strategy (ETS) based on 5-fold cross-validation was employed to automatically classify normal and abnormal cervical squamous cells from a multi-center dataset. First, we collected a dataset comprising 82 TCT samples from four hospitals and fine-tuned our model twice on the dataset with and without the ETS. Then, we compared the classifications obtained from the models with those provided by two skilled pathologists to discriminate the performance of the models in terms of classification accuracy and efficiency. Finally, paired sample t-tests were used to validate the consistency between the classification provided by the proposed methods and that of the pathologists. The results showed that ETS slightly, though not significantly, improved the classification accuracy compared with that of the pathologists: P 0 = 0.387>0.05 (DCNN without ETS vs. DCNN with ETS), P 1 = 0.771>0.05 (DCNN with ETS vs. pathologist 1), P 2 = 0.489>0.05 (DCNN with ETS vs. pathologist 2). The DCNN model was almost 6-fold faster than that of the pathologists. The accuracy of our automated scheme was similar to that of the pathologists, but a higher efficiency in the accurate identification of cervical squamous lesions was provided by the scheme. This result allows for wider and more efficient screening and may provide a replacement for pathologists in the future. Future research should address the viability of the practical implementation of such DCNN models in the laboratory setting.

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Conventional Machine Learning and Deep Learning Approach for Multi-Classification of Breast Cancer Histopathology Images—a Comparative Insight

Cited by 192 publications

References 49 publications

Deep Learning Applied for Histological Diagnosis of Breast Cancer

Deep Learning Applied for Histological Diagnosis of Breast Cancer

Identification of Gastritis Subtypes by Convolutional Neuronal Networks on Histological Images of Antrum and Corpus Biopsies

Artificial classification of cervical squamous lesions in ThinPrep cytologic tests using a deep convolutional neural network

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