Tissue-Type Classification With Uncertainty Quantification of Microwave and Ultrasound Breast Imaging: A Deep Learning Approach

Mojabi, Puyan; Khoshdel, Vahab; LoVetri, Joe

doi:10.1109/access.2020.3027805

Cited by 36 publications

(12 citation statements)

References 43 publications

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“…Some researchers are investigating the coupling of MWI with ultrasound transducers in hybrid microwave–acoustic imaging [ 126 , 127 ]. Another direction that is being explored is compressive sensing to improve the specificity, sensitivity, and accuracy of breast cancer diagnosis [ 11 ].…”

Section: Challenges and Future Research Directionsmentioning

confidence: 99%

Microwave Imaging for Early Breast Cancer Detection: Current State, Challenges, and Future Directions

et al. 2022

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Breast cancer is the most commonly diagnosed cancer type and is the leading cause of cancer-related death among females worldwide. Breast screening and early detection are currently the most successful approaches for the management and treatment of this disease. Several imaging modalities are currently utilized for detecting breast cancer, of which microwave imaging (MWI) is gaining quite a lot of attention as a promising diagnostic tool for early breast cancer detection. MWI is a noninvasive, relatively inexpensive, fast, convenient, and safe screening tool. The purpose of this paper is to provide an up-to-date survey of the principles, developments, and current research status of MWI for breast cancer detection. This paper is structured into two sections; the first is an overview of current MWI techniques used for detecting breast cancer, followed by an explanation of the working principle behind MWI and its various types, namely, microwave tomography and radar-based imaging. In the second section, a review of the initial experiments along with more recent studies on the use of MWI for breast cancer detection is presented. Furthermore, the paper summarizes the challenges facing MWI as a breast cancer detection tool and provides future research directions. On the whole, MWI has proven its potential as a screening tool for breast cancer detection, both as a standalone or complementary technique. However, there are a few challenges that need to be addressed to unlock the full potential of this imaging modality and translate it to clinical settings.

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Section: Challenges and Future Research Directionsmentioning

confidence: 99%

Microwave Imaging for Early Breast Cancer Detection: Current State, Challenges, and Future Directions

et al. 2022

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“…Following that, the k-nearest neighbor (KNN) method is used to improve overall classification performance. The authors propose a U-net-based segmentation in [ 38 ] for tissue type classification. The Gauss–Newton Inversion method is then used to reconstruct masses in order to target ultrasonic and dielectric properties.…”

Section: Related Workmentioning

confidence: 99%

Dilated Semantic Segmentation for Breast Ultrasonic Lesion Detection Using Parallel Feature Fusion

et al. 2021

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Breast cancer is becoming more dangerous by the day. The death rate in developing countries is rapidly increasing. As a result, early detection of breast cancer is critical, leading to a lower death rate. Several researchers have worked on breast cancer segmentation and classification using various imaging modalities. The ultrasonic imaging modality is one of the most cost-effective imaging techniques, with a higher sensitivity for diagnosis. The proposed study segments ultrasonic breast lesion images using a Dilated Semantic Segmentation Network (Di-CNN) combined with a morphological erosion operation. For feature extraction, we used the deep neural network DenseNet201 with transfer learning. We propose a 24-layer CNN that uses transfer learning-based feature extraction to further validate and ensure the enriched features with target intensity. To classify the nodules, the feature vectors obtained from DenseNet201 and the 24-layer CNN were fused using parallel fusion. The proposed methods were evaluated using a 10-fold cross-validation on various vector combinations. The accuracy of CNN-activated feature vectors and DenseNet201-activated feature vectors combined with the Support Vector Machine (SVM) classifier was 90.11 percent and 98.45 percent, respectively. With 98.9 percent accuracy, the fused version of the feature vector with SVM outperformed other algorithms. When compared to recent algorithms, the proposed algorithm achieves a better breast cancer diagnosis rate.

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“…Nowadays, deep learning technique (DLT) fascinates many attentions and has been used in medical imaging applications [26][27][28][29]. At present, researchers are motivated to apply this technique in electromagnetic imaging applications due to its abundant features [22,[30][31][32]. It can classify the target objects by training the convolutional neural networks (CNNs).…”

Section: Introductionmentioning

confidence: 99%

“…An automatic brain tumor detection and segmentation using a fully convolutional network (F-CNN) approach are demonstrated in [39]. In this approach, the U-net framework [32] with Adam optimization algorithm was used for segmentation of the image regions. From the segmentation, the tumor could be detected, but the accurate location of the tumor in the image is difficult to identify due to the shortage of unbiased predictor.…”

Section: Introductionmentioning

confidence: 99%

A YOLOv3 Deep Neural Network Model to Detect Brain Tumor in Portable Electromagnetic Imaging System

et al. 2021

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This paper presents the detection of brain tumors through the YOLOv3 deep neural network model in a portable electromagnetic (EM) imaging system. YOLOv3 is a popular object detection model with high accuracy and improved computational speed. Initially, the scattering parameters are collected from the nineantenna array setup with a tissue-mimicking head phantom, where one antenna acts as a transmitter and the other eight antennas act as receivers. The images are then reconstructed from the post-processed scattering parameters by applying the modified delay-multiply-and-sum algorithm that contains 416×416 pixels. Fifty sample images are collected from the different head regions through the EM imaging system. The images are later augmented to generate a final image data set for training, validation, and testing, where the data set contains 1000 images, including fifty samples with a single and double tumor. 80% of the images are utilized for training the network, whereas 10% are used for validation, and the rest 10% are utilized for testing purposes. The detection performance is investigated with the different image data sets. The achieved detection accuracy and F1 scores are 95.62% and 94.50%, respectively, which ensure better detection accuracy. The training accuracy and validation losses are 96.74% and 9.21%, respectively. The tumor detection with its location in different cases from the testing images is evaluated through YOLOv3, which demonstrates its potential in the portable electromagnetic head imaging system.

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Tissue-Type Classification With Uncertainty Quantification of Microwave and Ultrasound Breast Imaging: A Deep Learning Approach

Cited by 36 publications

References 43 publications

Microwave Imaging for Early Breast Cancer Detection: Current State, Challenges, and Future Directions

Microwave Imaging for Early Breast Cancer Detection: Current State, Challenges, and Future Directions

Dilated Semantic Segmentation for Breast Ultrasonic Lesion Detection Using Parallel Feature Fusion

A YOLOv3 Deep Neural Network Model to Detect Brain Tumor in Portable Electromagnetic Imaging System

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