A computer-aided approach for automatic detection of breast masses in digital mammogram via spectral clustering and support vector machine

Ketabi, Hossein; Ekhlasi, Ali; Ahmadi, Hessam

doi:10.1007/s13246-021-00977-5

Cited by 8 publications

(8 citation statements)

References 27 publications

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“…Automated techniques are being developed to analyze and diagnose breast mammograms with the goal of counteracting this variability and standardizing diagnostic procedures [ 14 , 15 ]. The rapid emergence of artificial intelligence (AI) and deep learning (DL) has significant implications for breast cancer diagnosis [ 16 , 17 , 18 ].…”

Section: Introductionmentioning

confidence: 99%

Connected-SegNets: A Deep Learning Model for Breast Tumor Segmentation from X-ray Images

Alkhaleefah

Tan

Chang

et al. 2022

Cancers

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Inspired by Connected-UNets, this study proposes a deep learning model, called Connected-SegNets, for breast tumor segmentation from X-ray images. In the proposed model, two SegNet architectures are connected with skip connections between their layers. Moreover, the cross-entropy loss function of the original SegNet has been replaced by the intersection over union (IoU) loss function in order to make the proposed model more robust against noise during the training process. As part of data preprocessing, a histogram equalization technique, called contrast limit adapt histogram equalization (CLAHE), is applied to all datasets to enhance the compressed regions and smooth the distribution of the pixels. Additionally, two image augmentation methods, namely rotation and flipping, are used to increase the amount of training data and to prevent overfitting. The proposed model has been evaluated on two publicly available datasets, specifically INbreast and the curated breast imaging subset of digital database for screening mammography (CBIS-DDSM). The proposed model has also been evaluated using a private dataset obtained from Cheng Hsin General Hospital in Taiwan. The experimental results show that the proposed Connected-SegNets model outperforms the state-of-the-art methods in terms of Dice score and IoU score. The proposed Connected-SegNets produces a maximum Dice score of 96.34% on the INbreast dataset, 92.86% on the CBIS-DDSM dataset, and 92.25% on the private dataset. Furthermore, the experimental results show that the proposed model achieves the highest IoU score of 91.21%, 87.34%, and 83.71% on INbreast, CBIS-DDSM, and the private dataset, respectively.

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Section: Introductionmentioning

confidence: 99%

Connected-SegNets: A Deep Learning Model for Breast Tumor Segmentation from X-ray Images

Alkhaleefah

Tan

Chang

et al. 2022

Cancers

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“…• As listed in Table 4, machine learning methods show some remarkable differences with methods in Tables 2 and 3. Clustering-based methods by Kamil et al [101] and Ketabi et al [102] cannot achieve accuracy rates higher than 94% on MIAS and 90% on DDSM. Sharma et al [113] achieved high performances in mass detection and classification on IRMA (specificity 99% and sensitivity 99%) and DDSM (specificity 96% and sensitivity 97%) using SVM.…”

Section: Discussionmentioning

confidence: 94%

“…After testing the model on the MIAS dataset, accuracy rates of 91.18% and 94.12% were achieved by K-means and fuzzy c-means algorithm, respectively. Ketabi et al [102] presented a model to detect breast masses. It consists of the combination of three different approaches: clustering, texture analysis and support vector machine.…”

Section: Clustering Techniques For Mammogram Analysismentioning

confidence: 99%

A Bottom-Up Review of Image Analysis Methods for Suspicious Region Detection in Mammograms

et al. 2021

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Breast cancer is one of the most common death causes amongst women all over the world. Early detection of breast cancer plays a critical role in increasing the survival rate. Various imaging modalities, such as mammography, breast MRI, ultrasound and thermography, are used to detect breast cancer. Though there is a considerable success with mammography in biomedical imaging, detecting suspicious areas remains a challenge because, due to the manual examination and variations in shape, size, other mass morphological features, mammography accuracy changes with the density of the breast. Furthermore, going through the analysis of many mammograms per day can be a tedious task for radiologists and practitioners. One of the main objectives of biomedical imaging is to provide radiologists and practitioners with tools to help them identify all suspicious regions in a given image. Computer-aided mass detection in mammograms can serve as a second opinion tool to help radiologists avoid running into oversight errors. The scientific community has made much progress in this topic, and several approaches have been proposed along the way. Following a bottom-up narrative, this paper surveys different scientific methodologies and techniques to detect suspicious regions in mammograms spanning from methods based on low-level image features to the most recent novelties in AI-based approaches. Both theoretical and practical grounds are provided across the paper sections to highlight the pros and cons of different methodologies. The paper’s main scope is to let readers embark on a journey through a fully comprehensive description of techniques, strategies and datasets on the topic.

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“…Stuck into the local optimum is a shortage of the original DHO algorithm [19]. In the following, a new modification has been proposed to recover this problem.…”

Section: Computational Intelligence and Neurosciencementioning

confidence: 99%

Automatic Breast Tumor Diagnosis in MRI Based on a Hybrid CNN and Feature-Based Method Using Improved Deer Hunting Optimization Algorithm

Vahedi

2021

Computational Intelligence and Neuroscience

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Breast cancer is an unusual mass of the breast texture. It begins with an abnormal change in cell structure. This disease may increase uncontrollably and affects neighboring textures. Early diagnosis of this cancer (abnormal cell changes) can help definitively treat it. Also, prevention of this cancer can help to decrease the high cost of medical caring for breast cancer patients. In recent years, the computer-aided technique is an important active field for automatic cancer detection. In this study, an automatic breast tumor diagnosis system is introduced. An improved Deer Hunting Optimization Algorithm (DHOA) is used as the optimization algorithm. The presented method utilized a hybrid feature-based technique and a new optimized convolutional neural network (CNN). Simulations are applied to the DCE-MRI dataset based on some performance indexes. The novel contribution of this paper is to apply the preprocessing stage to simplifying the classification. Besides, we used a new metaheuristic algorithm. Also, the feature extraction by Haralick texture and local binary pattern (LBP) is recommended. Due to the obtained results, the accuracy of this method is 98.89%, which represents the high potential and efficiency of this method.

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A computer-aided approach for automatic detection of breast masses in digital mammogram via spectral clustering and support vector machine

Cited by 8 publications

References 27 publications

Connected-SegNets: A Deep Learning Model for Breast Tumor Segmentation from X-ray Images

Connected-SegNets: A Deep Learning Model for Breast Tumor Segmentation from X-ray Images

A Bottom-Up Review of Image Analysis Methods for Suspicious Region Detection in Mammograms

Automatic Breast Tumor Diagnosis in MRI Based on a Hybrid CNN and Feature-Based Method Using Improved Deer Hunting Optimization Algorithm

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