Deep feature extraction and classification of breast ultrasound images

Kriti, .; Virmani, Jitendra; Agarwal, Ravinder

doi:10.1007/s11042-020-09337-z

Cited by 41 publications

(20 citation statements)

References 77 publications

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“…First, we utilize a CNN for the classification of breast ultrasound images and show the image location responsible for classification. Our classification results are comparable to the state of the art [30]. Second, we employ small systematic adversarial perturbations to distort the images such that the classification category does not change, but the location changes.…”

Section: Methodsmentioning

confidence: 52%

Explainable AI and susceptibility to adversarial attacks: a case study in classification of breast ultrasound images

Rasaee¹,

Rivaz²

2021

Preprint

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Ultrasound is a non-invasive imaging modality that can be conveniently used to classify suspicious breast nodules and potentially detect the onset of breast cancer. Recently, Convolutional Neural Networks (CNN) techniques have shown promising results in classifying ultrasound images of the breast into benign or malignant. However, CNN inference acts as a black-box model, and as such, its decision-making is not interpretable. Therefore, increasing effort has been dedicated to explaining this process, most notably through GRAD-CAM and other techniques that provide visual explanations into inner workings of CNNs. In addition to interpretation, these methods provide clinically important information, such as identifying the location for biopsy or treatment. In this work, we analyze how adversarial assaults that are practically undetectable may be devised to alter these importance maps dramatically. Furthermore, we will show that this change in the importance maps can come with or without altering the classification result, rendering them even harder to detect. As such, care must be taken when using these importance maps to shed light on the inner workings of deep learning. Finally, we utilize Multi-Task Learning (MTL) and propose a new network based on ResNet-50 to improve the classification accuracies. Our sensitivity and specificity is comparable to the state of the art results.

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Section: Methodsmentioning

confidence: 52%

Explainable AI and susceptibility to adversarial attacks: a case study in classification of breast ultrasound images

Rasaee¹,

Rivaz²

2021

Preprint

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“…The use of manual feature extraction techniques in traditional machine learning-based CABTD methods [10,[19][20][21][22][23][24][25][26][27][28][29][30][31][32][33] often requires domain expertise. In contrast to manual feature extraction techniques, the pre-trained DNN-based CABTD methods [16][17][18][37][38][39][40][41][42][43] extract the features automatically are effective and accurate when compared with traditional machine learning-based CABTD methods.…”

Section: Pre-trained Dnn-based Cabtd Methodsmentioning

confidence: 99%

“…The research on the computer-aided diagnosis of breast tumors using B-mode ultrasound images has been performed by several researchers. A detailed description of various CABTD methods from the last two decades is available [25,26]. The existing CABTD methods are broadly divided into two categories: traditional machine learning-based and pre-trained DNN-based strategies.…”

Section: Related Workmentioning

confidence: 99%

“…The components are removed from the global and local ROI structures at various depths (18, 50, and 101) fed to support vector machine (SVM) for better classification. [24], (b) Benign lesions from UDIAT dataset [25], (c) Malignant lesions from BUSI dataset [24] and (d) Malignant lesions from UDIAT dataset [25] contains hyperbolic regularization, which provides the suitable fuzzy sets for obtaining the best possible solution for pixel adjustment and image visual quality enhancement to standardize the predefined attribute levels. (vii) It induces membership grade modification by intuitionist fuzzy logic sets for accurate detection of infection spreads.…”

Section: Introductionmentioning

confidence: 99%

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Pre-Trained Deep Neural Network-Based Computer-Aided Breast Tumor Diagnosis Using ROI Structures

Srikanth¹,

Krithiga²

2023

Intelligent Automation &Amp; Soft Computing

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Deep neural network (DNN) based computer-aided breast tumor diagnosis (CABTD) method plays a vital role in the early detection and diagnosis of breast tumors. However, a Brightness mode (B-mode) ultrasound image derives training feature samples that make closer isolation toward the infection part. Hence, it is expensive due to a meta-heuristic search of features occupying the global region of interest (ROI) structures of input images. Thus, it may lead to the high computational complexity of the pre-trained DNN-based CABTD method. This paper proposes a novel ensemble pre-trained DNN-based CABTD method using global-and local-ROI-structures of B-mode ultrasound images. It conveys the additional consideration of a local-ROI-structures for further enhancing the pre-trained DNN-based CABTD method's breast tumor diagnostic performance without degrading its visual quality. The features are extracted at various depths (18, 50, and 101) from the global and local ROI structures and feed to support vector machine for better classification. From the experimental results, it has been observed that the combined local and global ROI structure of small depth residual network ResNet18 (0.8 in %) has produced significant improvement in pixel ratio as compared to ResNet50 (0.5 in %) and ResNet101 (0.3 in %), respectively. Subsequently, the pre-trained DNN-based CABTD methods have been tested by influencing local and global ROI structures to diagnose two specific breast tumors (Benign and Malignant) and improve the diagnostic accuracy (86%) compared to Dense Net, Alex Net, VGG Net, and Google Net. Moreover, it reduces the computational complexity due to the small depth residual network ResNet18, respectively.

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“…Flipping, as a geometric augmentation technique, often appears as a convenient tactic for natural images, and numerous research studies have been conducted in this field (16). However, in medical imaging study, flipping including both vertical and horizontal are adopted widely across several modalities of mammogram (32, 33), dermoscopy images (34, 35), chest CT scan (36,37), chest X-ray (15, 38), brain tumor MRI (21,39), tympanic membrane (40,41), breast cancer histopathology image (42,43), and breast cancer ultrasound images (44,45) which might be an obvious reason acquiring poor performance as the alteration may not result in clinical possible images. Though flipping a medical image such as an MRI scan would cause a scan one would almost never see in the clinical setting, it is often claimed to be an effective strategy (39,46).…”

Section: Geometric Augmentationmentioning

confidence: 99%

MNet-10: A robust shallow convolutional neural network model performing ablation study on medical images assessing the effectiveness of applying optimal data augmentation technique

et al. 2022

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Interpretation of medical images with a computer-aided diagnosis (CAD) system is arduous because of the complex structure of cancerous lesions in different imaging modalities, high degree of resemblance between inter-classes, presence of dissimilar characteristics in intra-classes, scarcity of medical data, and presence of artifacts and noises. In this study, these challenges are addressed by developing a shallow convolutional neural network (CNN) model with optimal configuration performing ablation study by altering layer structure and hyper-parameters and utilizing a suitable augmentation technique. Eight medical datasets with different modalities are investigated where the proposed model, named MNet-10, with low computational complexity is able to yield optimal performance across all datasets. The impact of photometric and geometric augmentation techniques on different datasets is also evaluated. We selected the mammogram dataset to proceed with the ablation study for being one of the most challenging imaging modalities. Before generating the model, the dataset is augmented using the two approaches. A base CNN model is constructed first and applied to both the augmented and non-augmented mammogram datasets where the highest accuracy is obtained with the photometric dataset. Therefore, the architecture and hyper-parameters of the model are determined by performing an ablation study on the base model using the mammogram photometric dataset. Afterward, the robustness of the network and the impact of different augmentation techniques are assessed by training the model with the rest of the seven datasets. We obtain a test accuracy of 97.34% on the mammogram, 98.43% on the skin cancer, 99.54% on the brain tumor magnetic resonance imaging (MRI), 97.29% on the COVID chest X-ray, 96.31% on the tympanic membrane, 99.82% on the chest computed tomography (CT) scan, and 98.75% on the breast cancer ultrasound datasets by photometric augmentation and 96.76% on the breast cancer microscopic biopsy dataset by geometric augmentation. Moreover, some elastic deformation augmentation methods are explored with the proposed model using all the datasets to evaluate their effectiveness. Finally, VGG16, InceptionV3, and ResNet50 were trained on the best-performing augmented datasets, and their performance consistency was compared with that of the MNet-10 model. The findings may aid future researchers in medical data analysis involving ablation studies and augmentation techniques.

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Deep feature extraction and classification of breast ultrasound images

Cited by 41 publications

References 77 publications

Explainable AI and susceptibility to adversarial attacks: a case study in classification of breast ultrasound images

Explainable AI and susceptibility to adversarial attacks: a case study in classification of breast ultrasound images

Pre-Trained Deep Neural Network-Based Computer-Aided Breast Tumor Diagnosis Using ROI Structures

MNet-10: A robust shallow convolutional neural network model performing ablation study on medical images assessing the effectiveness of applying optimal data augmentation technique

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