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Articles 1 - 24 of 24
Full-Text Articles in Engineering
Artificial Intelligence (Ai) And Nuclear Features From The Fine Needle Aspirated (Fna) Tissue Samples To Recognize Breast Cancer, Rumana Islam, Mohammed Tarique
Artificial Intelligence (Ai) And Nuclear Features From The Fine Needle Aspirated (Fna) Tissue Samples To Recognize Breast Cancer, Rumana Islam, Mohammed Tarique
Electrical and Computer Engineering Publications
Breast cancer is one of the paramount causes of new cancer cases worldwide annually. It is a malignant neoplasm that develops in the breast cells. The early screening of this disease is essential to prevent its metastasis. A mammogram X-ray image is the most common screening tool practiced currently when this disease is suspected; all the breast lesions identified are not malignant. The invasive fine needle aspiration (FNA) of a breast mass sample is the secondary screening tool to clinically examine cancerous lesions. The visual image analysis of the stained aspirated sample imposes a challenge for the cytologist to identify …
Deep Learning-Based Breast Cancer Diagnosis With Multiview Of Mammography Screening To Reduce False Positive Recall Rate, Meryem Altın Karagöz, Özkan Ufuk Nalbantoğlu, Derviş Karaboğa, Bahriye Akay, Alper Baştürk, Halil Ulutabanca, Serap Doğan, Damla Coşkun, Osman Demi̇r
Deep Learning-Based Breast Cancer Diagnosis With Multiview Of Mammography Screening To Reduce False Positive Recall Rate, Meryem Altın Karagöz, Özkan Ufuk Nalbantoğlu, Derviş Karaboğa, Bahriye Akay, Alper Baştürk, Halil Ulutabanca, Serap Doğan, Damla Coşkun, Osman Demi̇r
Turkish Journal of Electrical Engineering and Computer Sciences
Breast cancer is the most prevalent and crucial cancer type that should be diagnosed early to reduce mortality. Therefore, mammography is essential for early diagnosis owing to high-resolution imaging and appropriate visualization. However, the major problem of mammography screening is the high false positive recall rate for breast cancer diagnosis. High false positive recall rates psychologically affect patients, leading to anxiety, depression, and stress. Moreover, false positive recalls increase costs and create an unnecessary expert workload. Thus, this study proposes a deep learning based breast cancer diagnosis model to reduce false positive and false negative rates. The proposed model has …
A Comparative Study Of Yolo Models And A Transformer-Based Yolov5 Model For Mass Detection In Mammograms, Damla Coşkun, Dervi̇ş Karaboğa, Alper Baştürk, Bahri̇ye Akay, Özkan Ufuk Nalbantoğlu, Serap Doğan, İshak Paçal, Meryem Altin Karagöz
A Comparative Study Of Yolo Models And A Transformer-Based Yolov5 Model For Mass Detection In Mammograms, Damla Coşkun, Dervi̇ş Karaboğa, Alper Baştürk, Bahri̇ye Akay, Özkan Ufuk Nalbantoğlu, Serap Doğan, İshak Paçal, Meryem Altin Karagöz
Turkish Journal of Electrical Engineering and Computer Sciences
Breast cancer is a prevalent form of cancer across the globe, and if it is not diagnosed at an early stage it can be life-threatening. In order to aid in its diagnosis, detection, and classification, computer-aided detection (CAD) systems are employed. You Only Look Once (YOLO)-based CAD algorithms have become very popular owing to their highly accurate results for object detection tasks in recent years. Therefore, the most popular YOLO models are implemented to compare the performance in mass detection with various experiments on the INbreast dataset. In addition, a YOLO model with an integrated Swin Transformer in its backbone …
Special Section Editorial: Artificial Intelligence For Medical Imaging In Clinical Practice, Claudia Mello-Thoms, Karen Drukker, Sian Taylor-Phillips, Khan Iftekharuddin, Marios Gavrielides
Special Section Editorial: Artificial Intelligence For Medical Imaging In Clinical Practice, Claudia Mello-Thoms, Karen Drukker, Sian Taylor-Phillips, Khan Iftekharuddin, Marios Gavrielides
Electrical & Computer Engineering Faculty Publications
This editorial introduces the JMI Special Section on Artificial Intelligence for Medical Imaging in Clinical Practice.
Computer Aided Diagnosis System For Breast Cancer Using Deep Learning., Asma Baccouche
Computer Aided Diagnosis System For Breast Cancer Using Deep Learning., Asma Baccouche
Electronic Theses and Dissertations
The recent rise of big data technology surrounding the electronic systems and developed toolkits gave birth to new promises for Artificial Intelligence (AI). With the continuous use of data-centric systems and machines in our lives, such as social media, surveys, emails, reports, etc., there is no doubt that data has gained the center of attention by scientists and motivated them to provide more decision-making and operational support systems across multiple domains. With the recent breakthroughs in artificial intelligence, the use of machine learning and deep learning models have achieved remarkable advances in computer vision, ecommerce, cybersecurity, and healthcare. Particularly, numerous …
Breast Cancer-Caps: A Breast Cancer Screening System Based On Capsule Network Utilizing The Multiview Breast Thermal Infrared Images, Devanshu Tiwari, Manish Dixit, Kamlesh Gupta
Breast Cancer-Caps: A Breast Cancer Screening System Based On Capsule Network Utilizing The Multiview Breast Thermal Infrared Images, Devanshu Tiwari, Manish Dixit, Kamlesh Gupta
Turkish Journal of Electrical Engineering and Computer Sciences
This paper proposed an accurate and fully automated breast cancer early screening system called the "Breast Cancer-Caps". The capsule network is used in this approach for the cancer detection in breast utilizing the thermal infrared images for the first time. This capsule network is trained with the help of Dynamic as well as Static breast thermal images dataset consisting of left, right, frontal views along with a new multiview thermal images. These multiview breast thermal images are fabricated by concatenating the conventional left, frontal and right view breast thermal images. The other current and popular deep transfer learning models such …
Automated Classification Of Bi-Rads In Textual Mammography Reports, Mostafa Boroumandzadeh, Elham Parvinnia
Automated Classification Of Bi-Rads In Textual Mammography Reports, Mostafa Boroumandzadeh, Elham Parvinnia
Turkish Journal of Electrical Engineering and Computer Sciences
The main purpose of this paper is to process key information in medical text records and also classifypatients, per different levels of breast imaging-reporting and data system (BI-RADS). The BI-RADS is a scheme for thestandardization of breast imaging reports. Therefore, medical text mining is employed to classify mammography reportssupported BI-RADS. In this research, a new method is proposed for automated BI-RADS classifications extraction fromtextual reports and improves the therapeutic procedures. At first, a mammography lexicon is employed for choosingkeywords from medical text reports. Word2vec and term frequency inverse document frequency (TFIDF) techniques areused for extracting features, finally, they are combined …
Radar-Based Microwave Breast Cancer Detection System With A High-Performanceultrawide Band Antipodal Vivaldi Antenna, Hüseyi̇n Özmen, Muhammed Bahaddi̇n Kurt
Radar-Based Microwave Breast Cancer Detection System With A High-Performanceultrawide Band Antipodal Vivaldi Antenna, Hüseyi̇n Özmen, Muhammed Bahaddi̇n Kurt
Turkish Journal of Electrical Engineering and Computer Sciences
In this study, a novel ultrawide band (UWB) antipodal Vivaldi antenna with three pairs of slots was designed to be used as a sensor in microwave imaging systems for breast cancer detection. The proposed antenna operates in UWB frequency range of 3.05-12.2 GHz. FR4 was used as a dielectric material and as a substrate for forming the antenna that has a compact size of 36 mm x 36 mm x 1.6 mm. Frequency and time domain performance of the proposed antenna have been investigated and results show that it meets the requirements for UWB radar applications with linear phase response, …
Improved Cell Segmentation Using Deep Learning In Label-Free Optical Microscopyimages, Aydin Ayanzadeh, Özden Yalçin Özuysal, Devri̇m Pesen Okvur, Sevgi̇ Önal, Behçet Uğur Töreyi̇n, Devri̇m Ünay
Improved Cell Segmentation Using Deep Learning In Label-Free Optical Microscopyimages, Aydin Ayanzadeh, Özden Yalçin Özuysal, Devri̇m Pesen Okvur, Sevgi̇ Önal, Behçet Uğur Töreyi̇n, Devri̇m Ünay
Turkish Journal of Electrical Engineering and Computer Sciences
The recently popular deep neural networks (DNNs) have a significant effect on the improvement of segmentation accuracy from various perspectives, including robustness and completeness in comparison to conventional methods. We determined that the naive U-Net has some lacks in specific perspectives and there is high potential for further enhancements on the model. Therefore, we employed some modifications in different folds of the U-Net to overcome this problem. Based on the probable opportunity for improvement, we develop a novel architecture by using an alternative feature extractor in the encoder of U-Net and replacing the plain blocks with residual blocks in the …
Evaluation Of Terahertz Imaging For Breast Cancer Detection Using Image Morphing, Tanny Andrea Chavez Esparza
Evaluation Of Terahertz Imaging For Breast Cancer Detection Using Image Morphing, Tanny Andrea Chavez Esparza
Graduate Theses and Dissertations
This thesis proposes the use of a mesh morphing algorithm for the quantitative evaluation of terahertz (THz) images. This work differs from traditional evaluation methods based on qualitative evaluation because it provides a fair and quantitative measurement of the THz imaging system's performance. The objective of the algorithm is to match the alignment, shape, and resolution of the THz and reference pathology images. Therefore, the proposed morphing method provides a pathology reference for a pixel-by-pixel evaluation of the region classification in the THz image. To achieve this, the morphing algorithm aligns the images using the Pearson's correlation coefficient and reshapes …
3d Bioprinting Systems For The Study Of Mammary Development And Tumorigenesis, John Reid
3d Bioprinting Systems For The Study Of Mammary Development And Tumorigenesis, John Reid
Electrical & Computer Engineering Theses & Dissertations
Understanding the microenvironmental factors that control cell function, differentiation, and stem cell renewal represent the forefront of developmental and cancer biology. To accurately recreate and model these dynamic interactions in vitro requires both precision-controlled deposition of multiple cell types and well-defined three-dimensional (3D) extracellular matrix (ECM). To achieve this goal, we hypothesized that accessible bioprinting technology would eliminate the experimental inconsistency and random cell-organoid formation associated with manual cell-matrix embedding techniques commonly used for 3D, in vitro cell cultures. The first objective of this study was to adapt a commercially-available, 3D printer into a 3D bioprinter. Goal-based computer simulations were …
Hyperspectral Imaging And K-Means Classification For Histologic Evaluation Of Ductal Carcinoma In Situ, Yasser Khouj, Jeremy Dawson, James Coad, Linda Vona-Davis
Hyperspectral Imaging And K-Means Classification For Histologic Evaluation Of Ductal Carcinoma In Situ, Yasser Khouj, Jeremy Dawson, James Coad, Linda Vona-Davis
Faculty & Staff Scholarship
Hyperspectral imaging (HSI) is a non-invasive optical imaging modality that shows the potential to aid pathologists in breast cancer diagnoses cases. In this study, breast cancer tissues from different patients were imaged by a hyperspectral system to detect spectral differences between normal and breast cancer tissues. Tissue samples mounted on slides were identified from 10 different patients. Samples from each patient included both normal and ductal carcinoma tissue, both stained with hematoxylin and eosin stain and unstained. Slides were imaged using a snapshot HSI system, and the spec- tral reflectance differences were evaluated. Analysis of the spectral reflectance values indicated …
A Novel Multistage System For The Detection And Removal Of Pectoral Muscles Inmammograms, İdi̇l Işikli Esener, Semi̇h Ergi̇n, Tolga Yüksel
A Novel Multistage System For The Detection And Removal Of Pectoral Muscles Inmammograms, İdi̇l Işikli Esener, Semi̇h Ergi̇n, Tolga Yüksel
Turkish Journal of Electrical Engineering and Computer Sciences
In this paper, a novel multistage scheme for pectoral muscle removal from mammography images is proposed, and the performance of this system is verified using the publicly available Mammographic Image Analysis Society digital mammogram database. This database is composed of mediolateral oblique mammography images including three different tissue types (fatty, fatty-glandular, and dense-glandular) with three health status types (normal, benign cancer, and malignant cancer). In the implementation of the proposed system, a mammography image is first preprocessed by performing noise reduction background removal followed by artifact suppression processes. Then a presegmentation procedure is applied using region growing and line fitting …
Early Detection Of Metastatic Cancer Using Computational Analysis, Nuzhat Mansur
Early Detection Of Metastatic Cancer Using Computational Analysis, Nuzhat Mansur
Electrical Engineering Dissertations
Metastasis is the leading cause of cancer related deaths. Early detection of cancer cells can enable early disease diagnosis and stage specific therapeutics. Metastatic cancer cells have abnormal expression of certain proteins. One such protein is Epidermal Growth Factor Receptor (EGFR). Anti-EGFR aptamers have emerged as more effective probe molecules for selectively binding with EGFR compared to antibodies. Capturing cancer cells with aptamer is an emerging and developing technique for cancer cell isolation. Nanotextured substrates inspired by naturally occurring basement membranes are promising platform for triggering unique cell behavior. Along with the biochemical and physical techniques to probe cancer cell …
Breast Cancer Classification Of Mammographic Masses Using Circularity Max Metric, A New Method, Tae Keun Heo
Breast Cancer Classification Of Mammographic Masses Using Circularity Max Metric, A New Method, Tae Keun Heo
Electronic Theses and Dissertations
Breast cancer classification can be divided into two categories. The first category is a benign tumor, and the other is a malignant tumor. The main purpose of breast cancer classification is to classify abnormalities into benign or malignant classes and thus help physicians with further analysis by minimizing potential errors that can be made by fatigued or inexperienced physicians. This paper proposes a new shape metric based on the area ratio of a circle to classify mammographic images into benign and malignant class. Support Vector Machine is used as a machine learning tool for training and classification purposes. The improved …
Numerical Simulation Of Terahertz Wave Interaction With Breast Cancer Tumor Tissue Sections, Abayomi Omotola Omolewu
Numerical Simulation Of Terahertz Wave Interaction With Breast Cancer Tumor Tissue Sections, Abayomi Omotola Omolewu
Graduate Theses and Dissertations
This thesis presents numerical simulation of terahertz (THz) wave interaction with breast cancer tumor tissue sections. The obtained results are expressed in THz images of heterogeneous material that mimics the excised breast cancer tissue sections. The finite-element software package ANSYS High Frequency Structural Simulator (HFSS) was used in this work. HFSS is a full wave frequency domain three-dimensional (3D) electromagnetic simulation package. In this work, four breast cancer tissue models based on pathology images were simulated and images of the models were obtained at 1 THz. An incident Gaussian beam was raster scanned over tissue model configurations and the reflected …
Enhanced Diagnostic Accuracy Of Mammograms On A Mobile Device, Sharanya Padmanabhan
Enhanced Diagnostic Accuracy Of Mammograms On A Mobile Device, Sharanya Padmanabhan
Open Access Theses
With the death of a woman every 13 minutes in the US, and one every minute worldwide, due to breast cancer, the need for early detection cannot be overstated. Mammography is a boon for both early detection and screening of breast tumors. It is an imaging system that uses low dose (9mrem) x-rays for examining the breasts, by the electrons reflected from the tissues (thermoelectric effect). However, there are 20% false positives and 10% false negatives in current practice. Hence, there is a critical need for enhancing the accuracy of these mammograms. Towards this, this thesis was aimed at enhancing …
Extracting Fuzzy Rules For The Diagnosis Of Breast Cancer, Ali̇ Keleş, Aytürk Keleş
Extracting Fuzzy Rules For The Diagnosis Of Breast Cancer, Ali̇ Keleş, Aytürk Keleş
Turkish Journal of Electrical Engineering and Computer Sciences
About one million women are diagnosed with breast cancer every year. Breast cancer makes up one-third of all cancer diagnoses in women. Diagnosing breast cancer early is vital for successful treatment. Among the breast cancer screening methods available today, mammography is the most effective, although the low precision rate of breast biopsy caused by mammogram interpretation results in approximately 70% unnecessary biopsies with benign outcomes. The aim of this study was to extract strong diagnostic fuzzy rules for the inference engine of an expert system to be used for the diagnosis of breast cancer. These rules have been extracted through …
A New Intelligent Classifier For Breast Cancer Diagnosis Based On A Rough Set And Extreme Learning Machine: Rs + Elm, Yilmaz Kaya
A New Intelligent Classifier For Breast Cancer Diagnosis Based On A Rough Set And Extreme Learning Machine: Rs + Elm, Yilmaz Kaya
Turkish Journal of Electrical Engineering and Computer Sciences
Breast cancer is one of the leading causes of death among women all around the world. Therefore, true and early diagnosis of breast cancer is an important problem. The rough set (RS) and extreme learning machine (ELM) methods were used collectively in this study for the diagnosis of breast cancer. The unnecessary attributes were discarded from the dataset by means of the RS approach. The classification process by means of ELM was performed using the remaining attributes. The Wisconsin Breast Cancer dataset (WBCD), derived from the University of California Irvine machine learning database, was used for the purpose of testing …
Diagnosis Of Breast Cancer By Optical Image Analysis, Salim J. Attia, Jonathan Blackledge, Ziad M. Abood, I. R. Agool
Diagnosis Of Breast Cancer By Optical Image Analysis, Salim J. Attia, Jonathan Blackledge, Ziad M. Abood, I. R. Agool
Conference papers
We consider the process of object detection, recognition and classification in digital optical images of human breast cells with the aim of differentiating between normal and abnormal (cancerous) cells. The work is based on research into the development of a breast cancer screening system that can be used by cytologists to differentiate between benign and malignant types using images that are typical of those currently interpreted by cytologists world-wide. The approach considered is based on feature vectors which are of two types. We consider statistical features such as the mode, median, mean, and standard deviation and features composed of Euclidian …
Radio-Frequency Breast Cancer Imaging Results For A Simplified Cylindrical Phantom, Giuseppe Ruvio, Raffaele Solimene, Antonietta D'Alterio, Max Ammann, Rocco Pierri
Radio-Frequency Breast Cancer Imaging Results For A Simplified Cylindrical Phantom, Giuseppe Ruvio, Raffaele Solimene, Antonietta D'Alterio, Max Ammann, Rocco Pierri
Conference Papers
Microwave imaging is a pervasive research field and
is useful in numerous applicative diagnostic noninvasive contexts. This paper focuses on two aspects. First, we perform a numerical investigation to assess the role played by fundamental parameters (i.e. number of sensors, operating frequency bandwidth) on cancer detection. To this end, a simplified cylindrical phantom probed by ideal two-dimensional dipoles (i.e. infinitely long along the axis of invariance) is considered. Second, in order to focus on the role of the antennas, we analyze, still by numerical simulations and for a simplified breast model, how performances vary when a realistic antenna is adopted.
A Neural-Genetic Algorithm For Feature Selection And Breast Abnormality Classification In Digital Mammography, Ping Zhang, Brijesh Verma, Kuldeep Kumar
A Neural-Genetic Algorithm For Feature Selection And Breast Abnormality Classification In Digital Mammography, Ping Zhang, Brijesh Verma, Kuldeep Kumar
Kuldeep Kumar
Digital mammography is one of the most suitable methods for early detection of breast cancer. It uses digital mammograms to find suspicious areas. However, it is very difficult to distinguish benign and malignant cases, especially for the small size lesions in the early stage of cancer. This is reflected in the high percentage of unnecessary biopsies that are performed and many deaths caused by late detection or misdiagnosis. A computer based feature selection and classification system can provide a second opinion to the radiologists. This work proposes a neural-genetic algorithm for feature selection in conjunction with neural network based classifier. …
Neural Vs Statistical Classifier In Conjunction With Genetic Algorithm Feature Selection In Digital Mammography, Ping Zhang, Brijesh Verma, Kuldeep Kumar
Neural Vs Statistical Classifier In Conjunction With Genetic Algorithm Feature Selection In Digital Mammography, Ping Zhang, Brijesh Verma, Kuldeep Kumar
Kuldeep Kumar
Digital mammography is one of the most suitable methods for early detection of breast cancer. It uses digital mammograms to find suspicious areas containing benign and malignant microcalcifications. However, it is very difficult to distinguish benign and malignant microcalcifications. This is reflected in the high percentage of unnecessary biopsies that are performed and many deaths caused by late detection or misdiagnosis. A computer based feature selection and classification system can provide a second opinion to the radiologists in assessment of microcalcifications. The research proposes and investigates a neural-genetic algorithm for feature selection in conjunction with neural and statistical classifiers to …
Breast Cancer Mass Detection Using Difference Of Gaussians And Pulse Coupled Neural Networks, Donald A. Cournoyer
Breast Cancer Mass Detection Using Difference Of Gaussians And Pulse Coupled Neural Networks, Donald A. Cournoyer
Theses and Dissertations
CAD, serving as a second reader, has been shown to improve the success of radiologists at detecting breast cancer. This thesis will develop a new algorithm to identify masses in mammograms. The system developed for this thesis will be capable of assisting a radiologist in making decisions.