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Kakkar M, Patil JM, Trivedi V, Yadav A, Saha RK, Rao S, Vazhayil V, Pandya HJ, Mahadevan A, Shekhar H, Mercado-Shekhar KP. Hermite-scan imaging for differentiating glioblastoma from normal brain: Simulations and ex vivo studies for applications in intra-operative tumor identificationa). THE JOURNAL OF THE ACOUSTICAL SOCIETY OF AMERICA 2023; 154:3833-3841. [PMID: 38109407 DOI: 10.1121/10.0023952] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/09/2023] [Accepted: 11/28/2023] [Indexed: 12/20/2023]
Abstract
Hermite-scan (H-scan) imaging is a tissue characterization technique based on the analysis of raw ultrasound radio frequency (RF) echoes. It matches the RF echoes to Gaussian-weighted Hermite polynomials of various orders to extract information related to scatterer diameter. It provides a color map of large and small scatterers in the red and blue H-scan image channels, respectively. H-scan has been previously reported for characterizing breast, pancreatic, and thyroid tumors. The present work evaluated H-scan imaging to differentiate glioblastoma tumors from normal brain tissue ex vivo. First, we conducted 2-D numerical simulations using the k-wave toolbox to assess the performance of parameters derived from H-scan images of acoustic scatterers (15-150 μm diameters) and concentrations (0.2%-1% w/v). We found that the parameter intensity-weighted percentage of red (IWPR) was sensitive to changes in scatterer diameters independent of concentration. Next, we assessed the feasibility of using the IWPR parameter for differentiating glioblastoma and normal brain tissues (n = 11 samples per group). The IWPR parameter estimates for normal tissue (44.1% ± 1.4%) were significantly different (p < 0.0001) from those for glioblastoma (36.2% ± 0.65%). These findings advance the development of H-scan imaging for potential use in differentiating glioblastoma tumors from normal brain tissue during resection surgery.
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Affiliation(s)
- Manik Kakkar
- Department of Electrical Engineering, Indian Institute of Technology Gandhinagar, Gandhinagar, Gujarat 382355, India
| | - Jagruti M Patil
- Department of Biological Sciences and Engineering, Indian Institute of Technology Gandhinagar, Gandhinagar, Gujarat 382355, India
| | - Vishwas Trivedi
- Department of Electrical Engineering, Indian Institute of Technology Gandhinagar, Gandhinagar, Gujarat 382355, India
| | - Anushka Yadav
- Department of Electrical Engineering, Indian Institute of Technology Gandhinagar, Gandhinagar, Gujarat 382355, India
| | - Ratan K Saha
- Department of Applied Sciences, Indian Institute of Information Technology Allahabad, Prayagraj, Uttar Pradesh 211015, India
| | - Shilpa Rao
- Department of Neuropathology, National Institute of Mental Health and Neurosciences, Bangalore, Karnataka 560029, India
| | - Vikas Vazhayil
- Department of Neurosurgery, National Institute of Mental Health and Neurosciences, Bangalore, Karnataka 560029, India
| | - Hardik J Pandya
- Department of Electronic Systems Engineering, Indian Institute of Science, Bangalore, Karnataka 560012, India
| | - Anita Mahadevan
- Department of Neuropathology, National Institute of Mental Health and Neurosciences, Bangalore, Karnataka 560029, India
| | - Himanshu Shekhar
- Department of Electrical Engineering, Indian Institute of Technology Gandhinagar, Gandhinagar, Gujarat 382355, India
| | - Karla P Mercado-Shekhar
- Department of Biological Sciences and Engineering, Indian Institute of Technology Gandhinagar, Gandhinagar, Gujarat 382355, India
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Li S, Zhou Z, Wu S, Wu W. Ultrasound Homodyned-K Contrast-Weighted Summation Parametric Imaging Based on H-scan for Detecting Microwave Ablation Zones. ULTRASONIC IMAGING 2023; 45:119-135. [PMID: 36995065 DOI: 10.1177/01617346231162928] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/19/2023]
Abstract
The homodyned-K (HK) distribution is a generalized model of envelope statistics whose parameters α (the clustering parameter) and k (the coherent-to-diffuse signal ratio) can be used to monitor the thermal lesions. In this study, we proposed an ultrasound HK contrast-weighted summation (CWS) parametric imaging algorithm based on the H-scan technique and investigated the optimal window side length (WSL) of the HK parameters estimated by the XU estimator (an estimation method based on the first moment of the intensity and two log-moments, which was used in the proposed algorithm) through phantom simulations. H-scan diversified ultrasonic backscattered signals into low- and high-frequency passbands. After envelope detection and HK parameter estimation for each frequency band, the α and k parametric maps were obtained, respectively. According to the contrast between the target region and background, the (α or k) parametric maps of the dual-frequency band were weighted and summed, and then the CWS images were yielded by pseudo-color imaging. The proposed HK CWS parametric imaging algorithm was used to detect the microwave ablation coagulation zones of porcine liver ex vivo under different powers and treatment durations. The performance of the proposed algorithm was compared with that of the conventional HK parametric imaging and frequency diversity and compounding Nakagami imaging algorithms. For two-dimensional HK parametric imaging, it was found that a WSL equal to 4 pulse lengths of the transducer was sufficient for estimating the α and k parameters in terms of both parameter estimation stability and parametric imaging resolution. The HK CWS parametric imaging provided an improved contrast-to-noise ratio over conventional HK parametric imaging, and the HK αcws parametric imaging achieved the best accuracy and Dice score of coagulation zone detection.
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Affiliation(s)
- Sinan Li
- Department of Biomedical Engineering, Faculty of Environment and Life, Beijing University of Technology, Beijing, China
| | - Zhuhuang Zhou
- Department of Biomedical Engineering, Faculty of Environment and Life, Beijing University of Technology, Beijing, China
| | - Shuicai Wu
- Department of Biomedical Engineering, Faculty of Environment and Life, Beijing University of Technology, Beijing, China
| | - Weiwei Wu
- College of Biomedical Engineering, Capital Medical University, Beijing, China
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Liu Y, He B, Zhang Y, Lang X, Yao R, Pan L. A Study on a Parameter Estimator for the Homodyned K Distribution Based on Table Search for Ultrasound Tissue Characterization. ULTRASOUND IN MEDICINE & BIOLOGY 2023; 49:970-981. [PMID: 36631331 DOI: 10.1016/j.ultrasmedbio.2022.11.019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/18/2022] [Revised: 11/27/2022] [Accepted: 11/30/2022] [Indexed: 06/17/2023]
Abstract
OBJECTIVE The homodyned K (HK) distribution is considered to be the most suitable distribution in the context of tissue characterization; therefore, the search for a rapid and reliable parameter estimator for HK distribution is important. METHODS We propose a novel parameter estimator based on a table search (TS) for HK parameter estimates. The TS estimator can inherit the strength of conventional estimators by integrating various features and taking advantage of the TS method in a rapid and easy operation. Performance of the proposed TS estimator was evaluated and compared with that of XU (the estimation method based on X and U statistics) and artificial neural network (ANN) estimators. DISCUSSION The simulation results revealed that the TS estimator is superior to the XU and ANN estimators in terms of normalized standard deviations and relative root mean squared errors of parameter estimation, and is faster. Clinical experiments found that the area under the receiver operating curve for breast lesion classification using the parameters estimated by the TS estimator could reach 0.871. CONCLUSION The proposed TS estimator is more accurate, reliable and faster than the state-of-the-art XU and ANN estimators and has great potential for ultrasound tissue characterization based on the HK distribution.
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Affiliation(s)
- Yang Liu
- Department of Electronic Engineering, Information School, Yunnan University, Kunming, Yunnan, China
| | - Bingbing He
- Department of Electronic Engineering, Information School, Yunnan University, Kunming, Yunnan, China.
| | - Yufeng Zhang
- Department of Electronic Engineering, Information School, Yunnan University, Kunming, Yunnan, China
| | - Xun Lang
- Department of Electronic Engineering, Information School, Yunnan University, Kunming, Yunnan, China
| | - Ruihan Yao
- Department of Electronic Engineering, Information School, Yunnan University, Kunming, Yunnan, China
| | - Lingrui Pan
- Department of Electronic Engineering, Information School, Yunnan University, Kunming, Yunnan, China
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Basurto-Hurtado JA, Cruz-Albarran IA, Toledano-Ayala M, Ibarra-Manzano MA, Morales-Hernandez LA, Perez-Ramirez CA. Diagnostic Strategies for Breast Cancer Detection: From Image Generation to Classification Strategies Using Artificial Intelligence Algorithms. Cancers (Basel) 2022; 14:3442. [PMID: 35884503 PMCID: PMC9322973 DOI: 10.3390/cancers14143442] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2022] [Revised: 07/02/2022] [Accepted: 07/12/2022] [Indexed: 02/04/2023] Open
Abstract
Breast cancer is one the main death causes for women worldwide, as 16% of the diagnosed malignant lesions worldwide are its consequence. In this sense, it is of paramount importance to diagnose these lesions in the earliest stage possible, in order to have the highest chances of survival. While there are several works that present selected topics in this area, none of them present a complete panorama, that is, from the image generation to its interpretation. This work presents a comprehensive state-of-the-art review of the image generation and processing techniques to detect Breast Cancer, where potential candidates for the image generation and processing are presented and discussed. Novel methodologies should consider the adroit integration of artificial intelligence-concepts and the categorical data to generate modern alternatives that can have the accuracy, precision and reliability expected to mitigate the misclassifications.
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Affiliation(s)
- Jesus A. Basurto-Hurtado
- C.A. Mecatrónica, Facultad de Ingeniería, Campus San Juan del Río, Universidad Autónoma de Querétaro, Rio Moctezuma 249, San Cayetano, San Juan del Rio 76807, Mexico; (J.A.B.-H.); (I.A.C.-A.)
- Laboratorio de Dispositivos Médicos, Facultad de Ingeniería, Universidad Autónoma de Querétaro, Carretera a Chichimequillas S/N, Ejido Bolaños, Santiago de Querétaro 76140, Mexico
| | - Irving A. Cruz-Albarran
- C.A. Mecatrónica, Facultad de Ingeniería, Campus San Juan del Río, Universidad Autónoma de Querétaro, Rio Moctezuma 249, San Cayetano, San Juan del Rio 76807, Mexico; (J.A.B.-H.); (I.A.C.-A.)
- Laboratorio de Dispositivos Médicos, Facultad de Ingeniería, Universidad Autónoma de Querétaro, Carretera a Chichimequillas S/N, Ejido Bolaños, Santiago de Querétaro 76140, Mexico
| | - Manuel Toledano-Ayala
- División de Investigación y Posgrado de la Facultad de Ingeniería (DIPFI), Universidad Autónoma de Querétaro, Cerro de las Campanas S/N Las Campanas, Santiago de Querétaro 76010, Mexico;
| | - Mario Alberto Ibarra-Manzano
- Laboratorio de Procesamiento Digital de Señales, Departamento de Ingeniería Electrónica, Division de Ingenierias Campus Irapuato-Salamanca (DICIS), Universidad de Guanajuato, Carretera Salamanca-Valle de Santiago KM. 3.5 + 1.8 Km., Salamanca 36885, Mexico;
| | - Luis A. Morales-Hernandez
- C.A. Mecatrónica, Facultad de Ingeniería, Campus San Juan del Río, Universidad Autónoma de Querétaro, Rio Moctezuma 249, San Cayetano, San Juan del Rio 76807, Mexico; (J.A.B.-H.); (I.A.C.-A.)
| | - Carlos A. Perez-Ramirez
- Laboratorio de Dispositivos Médicos, Facultad de Ingeniería, Universidad Autónoma de Querétaro, Carretera a Chichimequillas S/N, Ejido Bolaños, Santiago de Querétaro 76140, Mexico
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Yao R, Zhang Y, Wu K, Li Z, He M, Fengyue B. Quantitative assessment for characterization of breast lesion tissues using adaptively decomposed ultrasound RF images. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2022.103559] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
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Existing and Emerging Breast Cancer Detection Technologies and Its Challenges: A Review. APPLIED SCIENCES-BASEL 2021. [DOI: 10.3390/app112210753] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Breast cancer is the most leading cancer occurring in women and is a significant factor in female mortality. Early diagnosis of breast cancer with Artificial Intelligent (AI) developments for breast cancer detection can lead to a proper treatment to affected patients as early as possible that eventually help reduce the women mortality rate. Reliability issues limit the current clinical detection techniques, such as Ultra-Sound, Mammography, and Magnetic Resonance Imaging (MRI) from screening images for precise elucidation. The capability to detect a tumor in early diagnosis, expensive, relatively long waiting time due to pandemic and painful procedure for a patient to perform. This article aims to review breast cancer screening methods and recent technological advancements systematically. In addition, this paper intends to explore the progression and challenges of AI in breast cancer detection. The next state of the art between image and signal processing will be presented, and their performance is compared. This review will facilitate the researcher to insight the view of breast cancer detection technologies advancement and its challenges.
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Kumar N, Tandon M, Chintamani C. Intraoperative Specimen Ultrasonography: Is It a Reliable Tool for Margin Assessment Following Breast Conservation Surgery for Breast Carcinoma? Cureus 2021; 13:e15806. [PMID: 34178555 PMCID: PMC8221645 DOI: 10.7759/cureus.15806] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 06/21/2021] [Indexed: 11/05/2022] Open
Abstract
BACKGROUND Assessment of margins after breast conservation surgery is an essential part of management in breast cancer and is important in prognostication of the patient. Various intra-operative techniques like frozen section and imprint cytology are in use to ensure negative margins but have their limitations in the fact that 3D evaluation is not feasible. These lead to false negatives and also are operator dependent. In order to obviate these shortcomings, various centers are using specimen imaging (specimen mammogram and ultrasonography). AIMS AND OBJECTIVES 1) To evaluate the accuracy of specimen ultrasonography in assessing the margins following breast conservation surgery (BCS). 2) To compare the accuracy of intra-operative specimen ultra-sonography with frozen section for assessment of excision margins following BCS. MATERIALS AND METHODS Sixty-two biopsy-proven patients with breast cancer who underwent BCS were included in this prospective study at a tertiary cancer care center. The oriented specimens were evaluated by specimen ultrasonography and later by frozen section. The final histopathology served as the gold standard. RESULTS Specimen ultrasonography is found to be superior to frozen section in providing detailed assessment of margins in patients undergoing breast conservation. Specimen ultrasonography was also able to detect additional lesions which might be missed on frozen section, especially the in-situ carcinoma.
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Affiliation(s)
- Niranjan Kumar
- Department of Surgery, Tata Main Hospital, Jamshedpur, IND
| | - Megha Tandon
- Department of General Surgery, Vardhman Mahavir Medical College and Safdarjung Hospital, Delhi, IND
| | - Chintamani Chintamani
- Department of General Surgery, Vardhman Mahavir Medical College and Safdarjung Hospital, Delhi, IND
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Hyperspectral image-based analysis of thermal damage for ex-vivo bovine liver utilizing radiofrequency ablation. Surg Oncol 2021; 38:101564. [PMID: 33865183 DOI: 10.1016/j.suronc.2021.101564] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2020] [Revised: 02/23/2021] [Accepted: 03/28/2021] [Indexed: 12/20/2022]
Abstract
BACKGROUND & OBJECTIVE Thermal ablation is the predominant methodology to treat liver tumors for segregating patients who are not permitted to have surgical intervention. However, noticing or predicting the size of the thermal strategies is a challenging endeavor. We aim to analyze the effects of ablation district volume following radiofrequency ablation (RFA) of ex-vivo liver exploiting a custom Hyperspectral Imaging (HSI) system. MATERIALS AND METHODS RFA was conducted on the ex-vivo bovine liver at focal and peripheral blood vessel sites and observed by Custom HSI system, which has been designed to assess the exactness and proficiency using visible and near-infrared wavelengths region for tissue thermal effect. The experiment comprised up to ten trials with RFA. The experiment was carried out in two stages to assess the percentage of the thermal effect on the investigated sample superficially and for the side penetration effect. Measuring the diffuse reflectance (Ŗd) of the sample to identify the spectral reflectance shift which could differentiate between normal and ablated tissue exploiting the designed cross-correlation algorithm for monitoring of thermal ablation. RESULTS Determination of the diffuse reflection (Ŗd) spectral signature responses from normal, thermal effected, and thermal ablation regions of the investigated liver sample. Where the ideal wavelength range at (600-640 nm) could discriminate between these different regions. Then, exploited the converted RGB image of the HS liver tissue after RFA for more validations which shows that the optimum wavelength for differentiation at (530-560 nm and 600-640 nm). Finally, applying statistical analysis to validate our results presenting that wavelength 600 nm had the highest standard deviation (δ) to differentiate between various thermally affected regions regarding the normal tissue and wavelength 640 nm shows the highest (δ) to differentiate between the ablated and normal regions. CONCLUSION The designed and implemented medical imaging system incorporated the hyperspectral camera capabilities with the associate cross-correlation algorithm that could successfully distinguish between the ablated and thermally affected regions to assist the surgery during the tumor therapy.
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Byra M, Jarosik P, Szubert A, Galperin M, Ojeda-Fournier H, Olson L, O’Boyle M, Comstock C, Andre M. Breast mass segmentation in ultrasound with selective kernel U-Net convolutional neural network. Biomed Signal Process Control 2020; 61:102027. [PMID: 34703489 PMCID: PMC8545275 DOI: 10.1016/j.bspc.2020.102027] [Citation(s) in RCA: 50] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
In this work, we propose a deep learning method for breast mass segmentation in ultrasound (US). Variations in breast mass size and image characteristics make the automatic segmentation difficult. To address this issue, we developed a selective kernel (SK) U-Net convolutional neural network. The aim of the SKs was to adjust network's receptive fields via an attention mechanism, and fuse feature maps extracted with dilated and conventional convolutions. The proposed method was developed and evaluated using US images collected from 882 breast masses. Moreover, we used three datasets of US images collected at different medical centers for testing (893 US images). On our test set of 150 US images, the SK-U-Net achieved mean Dice score of 0.826, and outperformed regular U-Net, Dice score of 0.778. When evaluated on three separate datasets, the proposed method yielded mean Dice scores ranging from 0.646 to 0.780. Additional fine-tuning of our better-performing model with data collected at different centers improved mean Dice scores by ~6%. SK-U-Net utilized both dilated and regular convolutions to process US images. We found strong correlation, Spearman's rank coefficient of 0.7, between the utilization of dilated convolutions and breast mass size in the case of network's expansion path. Our study shows the usefulness of deep learning methods for breast mass segmentation. SK-U-Net implementation and pre-trained weights can be found at github.com/mbyr/bus_seg.
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Affiliation(s)
- Michal Byra
- Department of Ultrasound, Institute of Fundamental Technological Research, Polish Academy of Sciences, Warsaw, Poland
- Department of Radiology, University of California, San Diego, USA
| | - Piotr Jarosik
- Department of Information and Computational Science, Institute of Fundamental Technological Research, Polish Academy of Sciences, Warsaw, Poland
| | - Aleksandra Szubert
- Maria Sklodowska-Curie Memorial Cancer Centre and Institute of Oncology, Warsaw, Poland
| | | | | | - Linda Olson
- Department of Radiology, University of California, San Diego, USA
| | - Mary O’Boyle
- Department of Radiology, University of California, San Diego, USA
| | | | - Michael Andre
- Department of Radiology, University of California, San Diego, USA
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Jarosik P, Klimonda Z, Lewandowski M, Byra M. Breast lesion classification based on ultrasonic radio-frequency signals using convolutional neural networks. Biocybern Biomed Eng 2020. [DOI: 10.1016/j.bbe.2020.04.002] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
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Chang CY, Srinivasan K, Chen MC, Chen SJ. SVM-Enabled Intelligent Genetic Algorithmic Model for Realizing Efficient Universal Feature Selection in Breast Cyst Image Acquired via Ultrasound Sensing Systems. SENSORS 2020; 20:s20020432. [PMID: 31940932 PMCID: PMC7013744 DOI: 10.3390/s20020432] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/29/2019] [Revised: 01/08/2020] [Accepted: 01/10/2020] [Indexed: 12/30/2022]
Abstract
In recent years, there are several cost-effective intelligent sensing systems such as ultrasound imaging systems for visualizing the internal body structures of the body. Further, such intelligent sensing systems such as ultrasound systems have been deployed by medical doctors around the globe for efficient detection of several diseases and disorders in the human body. Even though the ultrasound sensing system is a useful tool for obtaining the imagery of various body parts, there is always a possibility of inconsistencies in these images due to the variation in the settings of the system parameters. Therefore, in order to overcome such issues, this research devises an SVM-enabled intelligent genetic algorithmic model for choosing the universal features with four distinct settings of the parameters. Subsequently, the distinguishing characteristics of these features are assessed utilizing the Sorensen-Dice coefficient, t-test, and Pearson’s R measure. It is apparent from the results of the SVM-enabled intelligent genetic algorithmic model that this approach aids in the effectual selection of universal features for the breast cyst images. In addition, this approach also accomplishes superior accuracy in the classification of the ultrasound image for four distinct settings of the parameters.
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Affiliation(s)
- Chuan-Yu Chang
- Department of Computer Science and Information Engineering, National Yunlin University of Science and Technology, Yunlin 64002, Taiwan;
- Correspondence:
| | - Kathiravan Srinivasan
- School of Information Technology and Engineering, Vellore Institute of Technology (VIT), Vellore 632014, India;
| | - Mao-Cheng Chen
- Department of Computer Science and Information Engineering, National Yunlin University of Science and Technology, Yunlin 64002, Taiwan;
| | - Shao-Jer Chen
- Department of Medical Imaging, Buddhist Dalin Tzu Chi General Hospital, Chiayi 622, Taiwan;
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