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Ahmad I, Alqurashi F. Early Cancer Detection Using Deep Learning and Medical Imaging: A Survey. Crit Rev Oncol Hematol 2024:104528. [PMID: 39413940 DOI: 10.1016/j.critrevonc.2024.104528] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2024] [Accepted: 10/02/2024] [Indexed: 10/18/2024] Open
Abstract
Cancer, characterized by the uncontrolled division of abnormal cells that harm body tissues, necessitates early detection for effective treatment. Medical imaging is crucial for identifying various cancers, yet its manual interpretation by radiologists is often subjective, labour-intensive, and time-consuming. Consequently, there is a critical need for an automated decision-making process to enhance cancer detection and diagnosis. Previously, a lot of work was done on surveys of different cancer detection methods, and most of them were focused on specific cancers and limited techniques. This study presents a comprehensive survey of cancer detection methods. It entails a review of 99 research articles collected from the Web of Science, IEEE, and Scopus databases, published between 2020 and 2024. The scope of the study encompasses 12 types of cancer, including breast, cervical, ovarian, prostate, esophageal, liver, pancreatic, colon, lung, oral, brain, and skin cancers. This study discusses different cancer detection techniques, including medical imaging data, image preprocessing, segmentation, feature extraction, deep learning and transfer learning methods, and evaluation metrics. Eventually, we summarised the datasets and techniques with research challenges and limitations. Finally, we provide future directions for enhancing cancer detection techniques.
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Affiliation(s)
- Istiak Ahmad
- Department of Computer Science, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, 21589, Saudi Arabia; School of Information and Communication Technology, Griffith University, Queensland, 4111, Australia.
| | - Fahad Alqurashi
- Department of Computer Science, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, 21589, Saudi Arabia
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Vijayarajan SM, Manoj Kumar D, Sudha G, Reddy AB. Infrared thermal images using PCSAN-Net-DBOA: An approach of breast cancer classification. Microsc Res Tech 2024; 87:1742-1752. [PMID: 38501825 DOI: 10.1002/jemt.24550] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2023] [Revised: 02/02/2024] [Accepted: 02/26/2024] [Indexed: 03/20/2024]
Abstract
This manuscript proposes thermal images using PCSAN-Net-DBOA Initially, the input images are engaged from the database for mastology research with infrared image (DMR-IR) dataset for breast cancer classification. The adaptive distorted Gaussian matched-filter (ADGMF) was used in removing noise and increasing the quality of infrared thermal images. Next, these preprocessed images are given into one-dimensional quantum integer wavelet S-transform (OQIWST) for extracting Grayscale statistic features like standard deviation, mean, variance, entropy, kurtosis, and skewness. The extracted features are given into the pyramidal convolution shuffle attention neural network (PCSANN) for categorization. In general, PCSANN does not show any adaption optimization techniques to determine the optimal parameter to offer precise breast cancer categorization. This research proposes the dung beetle optimization algorithm (DBOA) to optimize the PCSANN classifier that accurately diagnoses breast cancer. The BCD-PCSANN-DBO method is implemented using Python. To classify breast cancer, performance metrics including accuracy, precision, recall, F1 score, error rate, RoC, and computational time are considered. Performance of the BCD-PCSANN-DBO approach attains 29.87%, 28.95%, and 27.92% lower computation time and 13.29%, 14.35%, and 20.54% greater RoC compared with existing methods like breast cancer diagnosis utilizing thermal infrared imaging and machine learning approaches(BCD-CNN), breast cancer classification from thermal images utilizing Grunwald-Letnikov assisted dragonfly algorithm-based deep feature selection (BCD-VGG16) and Breast cancer detection in thermograms using deep selection based on genetic algorithm and Gray Wolf Optimizer (BCD-SqueezeNet), respectively. RESEARCH HIGHLIGHTS: The input images are engaged from the breast cancer dataset for breast cancer classification. The ADQMF was used in removing noise and increasing the quality of infrared thermal images. The extracted features are given into the PCSANN for categorization. DBOA is proposed to optimize PCSANN classifier that classifies breast cancer precisely. The proposed BCD-PCSANN-DBO method is implemented using Python.
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Affiliation(s)
- S M Vijayarajan
- Department of Electronics and Communication Engineering, NPR College of Engineering & Technology, Dindigul, Tamil Nadu, India
| | - D Manoj Kumar
- Department of Electronics and Communication Engineering, SRM Institute of Science and Technology, Ramapuram Campus, Chennai, Tamilnadu, India
| | - G Sudha
- Department of Biomedical Engineering, Muthayammal Engineering College, Tamil Nadu, India
| | - A Basi Reddy
- Department of Computer Science and Engineering, School of Computing, Mohan Babu University, Tirupati, Andhra Pradesh, India
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Shojaedini SV, Abedini M, Monajemi M. Generative adversarial network: a statistical-based deep learning paradigm to improve detecting breast cancer in thermograms. Med Biol Eng Comput 2024; 62:1077-1087. [PMID: 38148414 DOI: 10.1007/s11517-023-02989-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2023] [Accepted: 12/05/2023] [Indexed: 12/28/2023]
Abstract
Thermography, as a harmless modality, thanks to its low equipment complexity in parallel with quick and cheap access, has been able to come up as a method with significant potential in the diagnosis of some cancers in recent years. However, the complexity of the images resulting from this method has caused the use of deep learning to interpret thermograms. A limiting factor in this process is the strong dependence of deep learning methods on the number of training data, which is a serious challenge in thermography due to the young age of this technology and the lack of available images. In this paper, an attempt is made to reduce the above challenge by utilizing the concept of statistical learning in such a way that the statistical distribution of the original data is estimated by using generative adversarial networks (i.e., GAN). Then, several fake images are reconstructed based on the estimated distribution in order to increase the training thermograms. Since the fake images are reconstructed based on similar statistics of real thermograms in each class, the effective features of each class are preserved to a significant extent in the reconstruction process. The use of this method indicates a significant improvement in the separation of healthy and cancerous thermograms compared to the benchmark method which does not use the concept of GAN in such a way that characteristics of sensitivity and accuracy are improved in ranges of 3-9% and 3-7%, respectively. In terms of specificity, although we have seen an improvement of up to 9%, in some cases, small drops of up to 2% have also been observed, which can still be justified due to the significant improvement in sensitivity and accuracy.
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Affiliation(s)
- Seyed Vahab Shojaedini
- Biomedical Engineering Department, Iranian Research Organization for Science & Technology, Tehran, Iran.
| | - Mehdi Abedini
- Department of Computer Engineering, Faculty of Engineering, Islamic Azad University, E-Campus, Tehran, Iran
| | - Mahsa Monajemi
- Department of Biomedical Engineering, Faculty of Engineering, Islamic Azad University, Qazvin Branch, Qazvin, Iran
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Jaganathan D, Balasubramaniam S, Sureshkumar V, Dhanasekaran S. Revolutionizing Breast Cancer Diagnosis: A Concatenated Precision through Transfer Learning in Histopathological Data Analysis. Diagnostics (Basel) 2024; 14:422. [PMID: 38396461 PMCID: PMC10887508 DOI: 10.3390/diagnostics14040422] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2024] [Revised: 02/03/2024] [Accepted: 02/07/2024] [Indexed: 02/25/2024] Open
Abstract
Breast cancer remains a significant global public health concern, emphasizing the critical role of accurate histopathological analysis in diagnosis and treatment planning. In recent years, the advent of deep learning techniques has showcased notable potential in elevating the precision and efficiency of histopathological data analysis. The proposed work introduces a novel approach that harnesses the power of Transfer Learning to capitalize on knowledge gleaned from pre-trained models, adapting it to the nuanced landscape of breast cancer histopathology. Our proposed model, a Transfer Learning-based concatenated model, exhibits substantial performance enhancements compared to traditional methodologies. Leveraging well-established pretrained models such as VGG-16, MobileNetV2, ResNet50, and DenseNet121-each Convolutional Neural Network architecture designed for classification tasks-this study meticulously tunes hyperparameters to optimize model performance. The implementation of a concatenated classification model is systematically benchmarked against individual classifiers on histopathological data. Remarkably, our concatenated model achieves an impressive training accuracy of 98%. The outcomes of our experiments underscore the efficacy of this four-level concatenated model in advancing the accuracy of breast cancer histopathological data analysis. By synergizing the strengths of deep learning and transfer learning, our approach holds the potential to augment the diagnostic capabilities of pathologists, thereby contributing to more informed and personalized treatment planning for individuals diagnosed with breast cancer. This research heralds a promising stride toward leveraging cutting-edge technology to refine the understanding and management of breast cancer, marking a significant advancement in the intersection of artificial intelligence and healthcare.
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Affiliation(s)
- Dhayanithi Jaganathan
- Department of Computer Science and Engineering, Sona College of Technology, Salem 636005, India;
| | | | - Vidhushavarshini Sureshkumar
- Department of Computer Science and Engineering, Faculty of Engineering and Technology, SRM Institute of Science and Technology, Vadapalani Campus, Chennai 600026, India;
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Yang L, Peng S, Yahya RO, Qian L. Cancer detection in breast cells using a hybrid method based on deep complex neural network and data mining. J Cancer Res Clin Oncol 2023; 149:13331-13344. [PMID: 37486394 DOI: 10.1007/s00432-023-05191-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2023] [Accepted: 07/16/2023] [Indexed: 07/25/2023]
Abstract
INTRODUCTION Diagnosis of cancer in breast cells is an important and vital issue in the field of medicine. In this context, the use of advanced methods such as deep complex neural networks and data mining can significantly improve the accuracy and speed of diagnosis. A hybrid approach that can be effective in breast cancer diagnosis is the use of deep complex neural networks and data mining. Due to their powerful nonlinear capabilities in extracting complex features from data, deep neural networks have a very good ability to detect patterns related to cancer. By analyzing millions of data related to breast cells and recognizing common and unusual patterns in them, these networks are able to diagnose cancer with high accuracy. Also, the use of data mining method plays an important role in this process. METHODOLOGY Using data mining algorithms and techniques, useful information can be extracted from the available data and the characteristics of healthy and cancerous cells can be separated. This information can be given as input to the deep neural network to achieve more accurate diagnosis. Another method to diagnose breast cancer is the use of thermography, which we use in this research along with data mining and deep learning. RESULTS Thermography uses an infrared camera to record the temperature of the target area. This method of breast cancer imaging is less expensive and completely safe compared to other methods. A total of 187 volunteers including 152 healthy people and 35 cancer patients were evaluated. Each person had ten thermographic images, resulting in a total of 1870 thermographic images. Four alternative deep complex neural network models, namely ResNet18, ResNet50, VGG19, and Xception, were used to identify thermal images, including benign and malignant images. CONCULSION The evaluation results showed that the use of a combined method based on deep complex neural network and data mining in the diagnosis of cancer in breast cells can bring a significant improvement in the accuracy and speed of diagnosis of this important disease.
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Affiliation(s)
- Ling Yang
- School of Informatics, Harbin Guangsha College, Harbin, 150025, Heilongjiang, China
| | - Shengguang Peng
- School of Engineering and Management, Pingxiang University, Pingxiang, 337055, Jiangxi, China.
| | - Rebaz Othman Yahya
- Department of Computer Science, College of Science, Cihan University-Erbil, Erbil, Iraq
| | - Leren Qian
- School of Computing and Augmented Intelligence, Arizona State University, Tempe, AZ, 85281, USA
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Rai HM, Yoo J. A comprehensive analysis of recent advancements in cancer detection using machine learning and deep learning models for improved diagnostics. J Cancer Res Clin Oncol 2023; 149:14365-14408. [PMID: 37540254 DOI: 10.1007/s00432-023-05216-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2023] [Accepted: 07/26/2023] [Indexed: 08/05/2023]
Abstract
PURPOSE There are millions of people who lose their life due to several types of fatal diseases. Cancer is one of the most fatal diseases which may be due to obesity, alcohol consumption, infections, ultraviolet radiation, smoking, and unhealthy lifestyles. Cancer is abnormal and uncontrolled tissue growth inside the body which may be spread to other body parts other than where it has originated. Hence it is very much required to diagnose the cancer at an early stage to provide correct and timely treatment. Also, manual diagnosis and diagnostic error may cause of the death of many patients hence much research are going on for the automatic and accurate detection of cancer at early stage. METHODS In this paper, we have done the comparative analysis of the diagnosis and recent advancement for the detection of various cancer types using traditional machine learning (ML) and deep learning (DL) models. In this study, we have included four types of cancers, brain, lung, skin, and breast and their detection using ML and DL techniques. In extensive review we have included a total of 130 pieces of literature among which 56 are of ML-based and 74 are from DL-based cancer detection techniques. Only the peer reviewed research papers published in the recent 5-year span (2018-2023) have been included for the analysis based on the parameters, year of publication, feature utilized, best model, dataset/images utilized, and best accuracy. We have reviewed ML and DL-based techniques for cancer detection separately and included accuracy as the performance evaluation metrics to maintain the homogeneity while verifying the classifier efficiency. RESULTS Among all the reviewed literatures, DL techniques achieved the highest accuracy of 100%, while ML techniques achieved 99.89%. The lowest accuracy achieved using DL and ML approaches were 70% and 75.48%, respectively. The difference in accuracy between the highest and lowest performing models is about 28.8% for skin cancer detection. In addition, the key findings, and challenges for each type of cancer detection using ML and DL techniques have been presented. The comparative analysis between the best performing and worst performing models, along with overall key findings and challenges, has been provided for future research purposes. Although the analysis is based on accuracy as the performance metric and various parameters, the results demonstrate a significant scope for improvement in classification efficiency. CONCLUSION The paper concludes that both ML and DL techniques hold promise in the early detection of various cancer types. However, the study identifies specific challenges that need to be addressed for the widespread implementation of these techniques in clinical settings. The presented results offer valuable guidance for future research in cancer detection, emphasizing the need for continued advancements in ML and DL-based approaches to improve diagnostic accuracy and ultimately save more lives.
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Affiliation(s)
- Hari Mohan Rai
- School of Computing, Gachon University, 1342 Seongnam-daero, Sujeong-gu, Seongnam-si, 13120, Gyeonggi-do, Republic of Korea.
| | - Joon Yoo
- School of Computing, Gachon University, 1342 Seongnam-daero, Sujeong-gu, Seongnam-si, 13120, Gyeonggi-do, Republic of Korea
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Ferreira CJDS, Caires IQDS, da Costa WJB, de Almeida SMV. Collagen content and C-X-C motif chemokine ligand 12 expression in neoplastic breast stroma. REVISTA DA ASSOCIACAO MEDICA BRASILEIRA (1992) 2023; 69:e20221210. [PMID: 37729354 PMCID: PMC10508945 DOI: 10.1590/1806-9282.20221210] [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: 06/05/2023] [Accepted: 06/08/2023] [Indexed: 09/22/2023]
Abstract
OBJECTIVE This study aimed to evaluate the expression of C-X-C motif chemokine ligand 12 and its C-X-C chemokine receptor type 4, and the tumor-stroma ratio using collagen stromal content of breast cancer samples, correlating it with clinicopathological data. METHODS Through a retrospective cohort study, samples were obtained from female patients, over 18 years of age, with the disease in stages 1-4, who underwent mastectomy or lumpectomy. The biopsies were provided by the Oncology sector of the Hospital das Clínicas of Universidade Federal de Pernambuco, Recife city, in 2011-2014, including samples of invasive ductal carcinoma, ductal carcinoma in situ, or benign changes (fibroadenoma and hypertrophy), which were analyzed between 2020 and 2022 by immunohistochemistry for the expression of stromal cell characteristics. Collagen content was tested by Gomori staining and digital analysis of images. RESULTS Absence of stromal expression of C-X-C motif chemokine ligand 12 was associated with longer disease-free survival (disease-free survival=0.481), and expression of C-X-C chemokine receptor type 4 was associated with lower disease-free survival. An association was observed between clinicopathological variables and stromal expression of chemokines, that is, an association of stromal C-X-C motif chemokine ligand 12 with histological grade, angiolymphatic invasion, and an association between C-X-C chemokine receptor type 4 expression and histological grade. Analyses of digital pixels images of collagen and tumor cells showed a lower percentage of collagen in the invasive ductal carcinoma samples (39%), unlike samples without neoplasms (78%). CONCLUSION Low expression of C-X-C motif chemokine ligand 12 may be associated with a worse prognosis for breast cancer. Collagen staining analyzed using digital images represents an opportunity for clinical application and is indicative of the prognosis of the tumor microenvironment in breast carcinoma.
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Trongtirakul T, Agaian S, Oulefki A. Automated tumor segmentation in thermographic breast images. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2023; 20:16786-16806. [PMID: 37920034 DOI: 10.3934/mbe.2023748] [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: 11/04/2023]
Abstract
Identifying and delineating suspicious regions in thermal breast images poses significant challenges for radiologists during the examination and interpretation of thermogram images. This paper aims to tackle concerns related to enhancing the differentiation between cancerous regions and the background to achieve uniformity in the intensity of breast cancer's (BC) existence. Furthermore, it aims to effectively segment tumors that exhibit limited contrast with the background and extract relevant features that can distinguish tumors from the surrounding tissue. A new cancer segmentation scheme comprised of two primary stages is proposed to tackle these challenges. In the first stage, an innovative image enhancement technique based on local image enhancement with a hyperbolization function is employed to significantly improve the quality and contrast of breast imagery. This technique enhances the local details and edges of the images while preserving global brightness and contrast. In the second stage, a dedicated algorithm based on an image-dependent weighting strategy is employed to accurately segment tumor regions within the given images. This algorithm assigns different weights to different pixels based on their similarity to the tumor region and uses a thresholding method to separate the tumor from the background. The proposed enhancement and segmentation methods were evaluated using the Database for Mastology Research (DMR-IR). The experimental results demonstrate remarkable performance, with an average segmentation accuracy, sensitivity, and specificity coefficient values of 97%, 80%, and 99%, respectively. These findings convincingly establish the superiority of the proposed method over state-of-the-art techniques. The obtained results demonstrate the potential of the proposed method to aid in the early detection of breast cancer through improved diagnosis and interpretation of thermogram images.
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Affiliation(s)
- Thaweesak Trongtirakul
- Faculty of Industrial Education, Rajamangala University of Technology Phra Nakhon, 399 Samsen Rd. Vachira Phayaban, Dusit, Bangkok 10300, Thailand
| | - Sos Agaian
- Graduate Center, City University of New York, 365 Fifth Ave., New York, NY 10016, USA
| | - Adel Oulefki
- Centre de Développement des Technologies Avancées - CDTA, PO. Box 16018, Algiers, Algeria
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A review of deep learning-based multiple-lesion recognition from medical images: classification, detection and segmentation. Comput Biol Med 2023; 157:106726. [PMID: 36924732 DOI: 10.1016/j.compbiomed.2023.106726] [Citation(s) in RCA: 20] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2022] [Revised: 02/07/2023] [Accepted: 02/27/2023] [Indexed: 03/05/2023]
Abstract
Deep learning-based methods have become the dominant methodology in medical image processing with the advancement of deep learning in natural image classification, detection, and segmentation. Deep learning-based approaches have proven to be quite effective in single lesion recognition and segmentation. Multiple-lesion recognition is more difficult than single-lesion recognition due to the little variation between lesions or the too wide range of lesions involved. Several studies have recently explored deep learning-based algorithms to solve the multiple-lesion recognition challenge. This paper includes an in-depth overview and analysis of deep learning-based methods for multiple-lesion recognition developed in recent years, including multiple-lesion recognition in diverse body areas and recognition of whole-body multiple diseases. We discuss the challenges that still persist in the multiple-lesion recognition tasks by critically assessing these efforts. Finally, we outline existing problems and potential future research areas, with the hope that this review will help researchers in developing future approaches that will drive additional advances.
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Breast Cancer Diagnosis in Thermography Using Pre-Trained VGG16 with Deep Attention Mechanisms. Symmetry (Basel) 2023. [DOI: 10.3390/sym15030582] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/25/2023] Open
Abstract
One of the most prevalent cancers in women is breast cancer. The mortality rate related to this disease can be decreased by early, accurate diagnosis to increase the chance of survival. Infrared thermal imaging is one of the breast imaging modalities in which the temperature of the breast tissue is measured using a screening tool. The previous studies did not use pre-trained deep learning (DL) with deep attention mechanisms (AMs) on thermographic images for breast cancer diagnosis. Using thermal images from the Database for Research Mastology with Infrared Image (DMR-IR), the study investigates the use of a pre-trained Visual Geometry Group with 16 layers (VGG16) with AMs that can produce good diagnosis performance utilizing the thermal images of breast cancer. The symmetry of the three models resulting from the combination of VGG16 with three types of AMs is evident in all its stages in methodology. The models were compared to state-of-art breast cancer diagnosis approaches and tested for accuracy, sensitivity, specificity, precision, F1-score, AUC score, and Cohen’s kappa. The test accuracy rates for the AMs using the VGG16 model on the breast thermal dataset were encouraging, at 99.80%, 99.49%, and 99.32%. Test accuracy for VGG16 without AMs was 99.18%, whereas test accuracy for VGG16 with AMs improved by 0.62%. The proposed approaches also performed better than previous approaches examined in the related studies.
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Maurya S, Tiwari S, Mothukuri MC, Tangeda CM, Nandigam RNS, Addagiri DC. A review on recent developments in cancer detection using Machine Learning and Deep Learning models. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2022.104398] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
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Fan W, Shangguan W, Bouguila N. Continuous image anomaly detection based on contrastive lifelong learning. APPL INTELL 2023. [DOI: 10.1007/s10489-022-04401-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2023]
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Bansal R, Collison S, Krishnan L, Aggarwal B, Vidyasagar M, Kakileti ST, Manjunath G. A prospective evaluation of breast thermography enhanced by a novel machine learning technique for screening breast abnormalities in a general population of women presenting to a secondary care hospital. Front Artif Intell 2023; 5:1050803. [PMID: 36686848 PMCID: PMC9846488 DOI: 10.3389/frai.2022.1050803] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2022] [Accepted: 11/28/2022] [Indexed: 01/06/2023] Open
Abstract
Objective Artificial intelligence-enhanced breast thermography is being evaluated as an ancillary modality in the evaluation of breast disease. The objective of this study was to evaluate the clinical performance of Thermalytix, a CE-marked, AI-based thermal imaging test, with respect to conventional mammography. Methods A prospective, comparative study performed between 15 December 2018 and 06 January 2020 evaluated the performance of Thermalytix in 459 women with both dense and nondense breast tissue. Both symptomatic and asymptomatic women, aged 30-80 years, presenting to the hospital underwent Thermalytix followed by 2-D mammography and appropriate confirmatory investigations to confirm malignancy. The radiologist interpreting the mammograms and the technician using the Thermalytix tool were blinded to the others' findings. The statistical analysis was performed by a third party. Results A total of 687 women were recruited, of whom 459 fulfilled the inclusion criteria. Twenty-one malignancies were detected (21/459, 4.6%). The overall sensitivity of Thermalytix was 95.24% (95% CI, 76.18-99.88), and the specificity was 88.58% (95% CI, 85.23-91.41). In women with dense breasts (n = 168, 36.6%), the sensitivity was 100% (95% CI, 69.15-100), and the specificity was 81.65% (95% CI, 74.72-87.35). Among these 168 women, 37 women (22%) were reported as BI-RADS 0 on mammography; in this subset, the sensitivity of Thermalytix was 100% (95% CI, 69.15-100), and the specificity was 77.22% (95% CI, 69.88-83.50). Conclusion Thermalytix showed acceptable sensitivity and specificity with respect to mammography in the overall patient population. Thermalytix outperformed mammography in women with dense breasts and those reported as BI-RADS 0.
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Affiliation(s)
- Richa Bansal
- Radiology Services, Max Super Speciality Hospital, Saket, New Delhi, India
| | - Sathiakar Collison
- Department of Clinical Affairs, Niramai Health Analytix Pvt. Ltd., Bangalore, India,*Correspondence: Sathiakar Collison
| | - Lakshmi Krishnan
- Department of Clinical Affairs, Niramai Health Analytix Pvt. Ltd., Bangalore, India
| | - Bharat Aggarwal
- Radiology Services, Max Super Speciality Hospital, Saket, New Delhi, India
| | | | - Siva Teja Kakileti
- Department of Clinical Affairs, Niramai Health Analytix Pvt. Ltd., Bangalore, India
| | - Geetha Manjunath
- Department of Clinical Affairs, Niramai Health Analytix Pvt. Ltd., Bangalore, India
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Pérez-Martín J, Sánchez-Cauce R. Quality analysis of a breast thermal images database. Health Informatics J 2023; 29:14604582231153779. [PMID: 36731024 DOI: 10.1177/14604582231153779] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
The study and early detection of breast cancer are key for its treatment. We carry out an exhaustive analysis of the most used database for mastology research with infrared images, analyzing the anomalies according to five quality dimensions: completeness, correctness, concordance, plausibility, and currency. We established control queries that looked for these anomalies and that can be used to ensure the quality of the database. Finally, we briefly review the more than 40 papers that use this database and that do not mention any of these anomalies. When analyzing the database, we found 365 anomalies related to personal and clinical data, and thermal images. The errors found in our research may lead to a modification of the results and conclusions made in the articles found in the literature, serve as a basis for improvements in the quality of the database, and help future researchers to work with it.
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Affiliation(s)
- Jorge Pérez-Martín
- Department of Artificial Intelligence, 16757Universidad Nacional de Educación a Distancia (UNED), Madrid, Spain
| | - Raquel Sánchez-Cauce
- Department of Artificial Intelligence, 16757Universidad Nacional de Educación a Distancia (UNED), Madrid, Spain
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Fu Q, Dong H. Spiking Neural Network Based on Multi-Scale Saliency Fusion for Breast Cancer Detection. ENTROPY 2022; 24:e24111543. [PMID: 36359633 PMCID: PMC9689387 DOI: 10.3390/e24111543] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/01/2022] [Revised: 10/17/2022] [Accepted: 10/21/2022] [Indexed: 05/22/2023]
Abstract
Deep neural networks have been successfully applied in the field of image recognition and object detection, and the recognition results are close to or even superior to those from human beings. A deep neural network takes the activation function as the basic unit. It is inferior to the spiking neural network, which takes the spiking neuron model as the basic unit in the aspect of biological interpretability. The spiking neural network is considered as the third-generation artificial neural network, which is event-driven and has low power consumption. It modulates the process of nerve cells from receiving a stimulus to firing spikes. However, it is difficult to train spiking neural network directly due to the non-differentiable spiking neurons. In particular, it is impossible to train a spiking neural network using the back-propagation algorithm directly. Therefore, the application scenarios of spiking neural network are not as extensive as deep neural network, and a spiking neural network is mostly used in simple image classification tasks. This paper proposed a spiking neural network method for the field of object detection based on medical images using the method of converting a deep neural network to spiking neural network. The detection framework relies on the YOLO structure and uses the feature pyramid structure to obtain the multi-scale features of the image. By fusing the high resolution of low-level features and the strong semantic information of high-level features, the detection precision of the network is improved. The proposed method is applied to detect the location and classification of breast lesions with ultrasound and X-ray datasets, and the results are 90.67% and 92.81%, respectively.
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Roy S, Meena T, Lim SJ. Demystifying Supervised Learning in Healthcare 4.0: A New Reality of Transforming Diagnostic Medicine. Diagnostics (Basel) 2022; 12:2549. [PMID: 36292238 PMCID: PMC9601517 DOI: 10.3390/diagnostics12102549] [Citation(s) in RCA: 28] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2022] [Revised: 10/17/2022] [Accepted: 10/18/2022] [Indexed: 11/17/2022] Open
Abstract
The global healthcare sector continues to grow rapidly and is reflected as one of the fastest-growing sectors in the fourth industrial revolution (4.0). The majority of the healthcare industry still uses labor-intensive, time-consuming, and error-prone traditional, manual, and manpower-based methods. This review addresses the current paradigm, the potential for new scientific discoveries, the technological state of preparation, the potential for supervised machine learning (SML) prospects in various healthcare sectors, and ethical issues. The effectiveness and potential for innovation of disease diagnosis, personalized medicine, clinical trials, non-invasive image analysis, drug discovery, patient care services, remote patient monitoring, hospital data, and nanotechnology in various learning-based automation in healthcare along with the requirement for explainable artificial intelligence (AI) in healthcare are evaluated. In order to understand the potential architecture of non-invasive treatment, a thorough study of medical imaging analysis from a technical point of view is presented. This study also represents new thinking and developments that will push the boundaries and increase the opportunity for healthcare through AI and SML in the near future. Nowadays, SML-based applications require a lot of data quality awareness as healthcare is data-heavy, and knowledge management is paramount. Nowadays, SML in biomedical and healthcare developments needs skills, quality data consciousness for data-intensive study, and a knowledge-centric health management system. As a result, the merits, demerits, and precautions need to take ethics and the other effects of AI and SML into consideration. The overall insight in this paper will help researchers in academia and industry to understand and address the future research that needs to be discussed on SML in the healthcare and biomedical sectors.
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Affiliation(s)
- Sudipta Roy
- Artificial Intelligence & Data Science, Jio Institute, Navi Mumbai 410206, India
| | - Tanushree Meena
- Artificial Intelligence & Data Science, Jio Institute, Navi Mumbai 410206, India
| | - Se-Jung Lim
- Division of Convergence, Honam University, 120, Honamdae-gil, Gwangsan-gu, Gwangju 62399, Korea
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17
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Ensafi M, Keyvanpour MR, Shojaedini SV. A New method for promote the performance of deep learning paradigm in diagnosing breast cancer: improving role of fusing multiple views of thermography images. HEALTH AND TECHNOLOGY 2022; 12:1097-1107. [PMID: 36254270 PMCID: PMC9556139 DOI: 10.1007/s12553-022-00702-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2022] [Accepted: 09/22/2022] [Indexed: 11/14/2022]
Abstract
Purpose Breast cancer is one of the deadliest cancers among women worldwide which its early detection may significantly reduce its mortality rate. Thermgraphy is a new, non-invasive, non-painful, and low-cost modality that detects abnormalities by detecting heat from the breast surface. Method Recent research has applied deep learning to early breast cancer diagnosis via thermography, using only the frontal view of thermograms. We combine several views of thermal images to improve the performance of pre-trained deep learning architectures in this article. This goal is achieved by combining frontal-45 data with lateral-45 and lateral45 thermograms to construct a detection model that utilizes transfer learning. Result Research in this area uses the Database for Mastology Research (DMR) with infrared images. In this study, transfer based deep learning methods are demonstrated to be effective in fusing several views of thermograms to diagnose breast cancer in a manner that can result in a sensitivity increase of 2-15 percent and a specificity increase of 2-30 percent compared to other deep learning-based or handcrafted schemes. Conclusion Using multiple views of thermograms and transfer learning, this paper proposes a method for improving breast cancer diagnosis. Using methods based on deep learning and methods based on hand-crafted features, we evaluated the performance of the proposed model. Using the obtained results as a basis for future research, the proposed design can be improved and developed as a valid approach in interpreting breast thermography images.
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Affiliation(s)
- Mahsa Ensafi
- Faculty of Engineering and Technology, Alzahra University, Tehran, Iran
| | | | - Seyed Vahab Shojaedini
- Department of Biomedical Engineering, Iranian Research Organization for Science and Technology, Tehran, Iran
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18
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Park S, Ahn S, Kim JY, Kim J, Han HJ, Hwang D, Park J, Park HS, Park S, Kim GM, Sohn J, Jeong J, Song YU, Lee H, Kim SI. Blood Test for Breast Cancer Screening through the Detection of Tumor-Associated Circulating Transcripts. Int J Mol Sci 2022; 23:ijms23169140. [PMID: 36012405 PMCID: PMC9409068 DOI: 10.3390/ijms23169140] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2022] [Revised: 08/05/2022] [Accepted: 08/10/2022] [Indexed: 11/29/2022] Open
Abstract
Liquid biopsy has been emerging for early screening and treatment monitoring at each cancer stage. However, the current blood-based diagnostic tools in breast cancer have not been sufficient to understand patient-derived molecular features of aggressive tumors individually. Herein, we aimed to develop a blood test for the early detection of breast cancer with cost-effective and high-throughput considerations in order to combat the challenges associated with precision oncology using mRNA-based tests. We prospectively evaluated 719 blood samples from 404 breast cancer patients and 315 healthy controls, and identified 10 mRNA transcripts whose expression is increased in the blood of breast cancer patients relative to healthy controls. Modeling of the tumor-associated circulating transcripts (TACTs) is performed by means of four different machine learning techniques (artificial neural network (ANN), decision tree (DT), logistic regression (LR), and support vector machine (SVM)). The ANN model had superior sensitivity (90.2%), specificity (80.0%), and accuracy (85.7%) compared with the other three models. Relative to the value of 90.2% achieved using the TACT assay on our test set, the sensitivity values of other conventional assays (mammogram, CEA, and CA 15-3) were comparable or much lower, at 89%, 7%, and 5%, respectively. The sensitivity, specificity, and accuracy of TACTs were appreciably consistent across the different breast cancer stages, suggesting the potential of the TACTs assay as an early diagnosis and prediction of poor outcomes. Our study potentially paves the way for a simple and accurate diagnostic and prognostic tool for liquid biopsy.
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Affiliation(s)
- Sunyoung Park
- Department of Biomedical Laboratory Science, College of Health Sciences, Yonsei University, Wonju 26493, Korea
| | - Sungwoo Ahn
- Department of Biomedical Laboratory Science, College of Health Sciences, Yonsei University, Wonju 26493, Korea
| | - Jee Ye Kim
- Department of Surgery, Yonsei University College of Medicine, Seoul 03722, Korea
| | - Jungho Kim
- Department of Clinical Laboratory Science, College of Health Sciences, Catholic University of Pusan, Busan 46252, Korea
| | - Hyun Ju Han
- Avison Biomedical Research Center, Yonsei University College of Medicine, Seoul 03722, Korea
| | - Dasom Hwang
- Department of Biomedical Laboratory Science, College of Health Sciences, Yonsei University, Wonju 26493, Korea
| | - Jungmin Park
- Department of Surgery, Yonsei University College of Medicine, Seoul 03722, Korea
| | - Hyung Seok Park
- Department of Surgery, Yonsei University College of Medicine, Seoul 03722, Korea
| | - Seho Park
- Department of Surgery, Yonsei University College of Medicine, Seoul 03722, Korea
| | - Gun Min Kim
- Department of Medical Oncology, Yonsei University College of Medicine, Seoul 03722, Korea
| | - Joohyuk Sohn
- Department of Medical Oncology, Yonsei University College of Medicine, Seoul 03722, Korea
| | - Joon Jeong
- Department of Surgery, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul 06273, Korea
| | - Yong Uk Song
- Division of Business Administration, College of Government and Business, Yonsei University, Wonju 26493, Korea
| | - Hyeyoung Lee
- Department of Biomedical Laboratory Science, College of Health Sciences, Yonsei University, Wonju 26493, Korea
- Correspondence: (H.L.); (S.I.K.)
| | - Seung Il Kim
- Department of Surgery, Yonsei University College of Medicine, Seoul 03722, Korea
- Correspondence: (H.L.); (S.I.K.)
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19
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din NMU, Dar RA, Rasool M, Assad A. Breast cancer detection using deep learning: Datasets, methods, and challenges ahead. Comput Biol Med 2022; 149:106073. [DOI: 10.1016/j.compbiomed.2022.106073] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2022] [Revised: 08/21/2022] [Accepted: 08/27/2022] [Indexed: 12/22/2022]
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20
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Mishra L, Verma S. Graph Attention Autoencoder Inspired CNN based Brain Tumor Classification using MRI. Neurocomputing 2022. [DOI: 10.1016/j.neucom.2022.06.107] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/12/2023]
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21
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Malakar S, Roy SD, Das S, Sen S, Velásquez JD, Sarkar R. Computer Based Diagnosis of Some Chronic Diseases: A Medical Journey of the Last Two Decades. ARCHIVES OF COMPUTATIONAL METHODS IN ENGINEERING : STATE OF THE ART REVIEWS 2022; 29:5525-5567. [PMID: 35729963 PMCID: PMC9199478 DOI: 10.1007/s11831-022-09776-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/16/2022] [Accepted: 05/22/2022] [Indexed: 06/15/2023]
Abstract
Disease prediction from diagnostic reports and pathological images using artificial intelligence (AI) and machine learning (ML) is one of the fastest emerging applications in recent days. Researchers are striving to achieve near-perfect results using advanced hardware technologies in amalgamation with AI and ML based approaches. As a result, a large number of AI and ML based methods are found in the literature. A systematic survey describing the state-of-the-art disease prediction methods, specifically chronic disease prediction algorithms, will provide a clear idea about the recent models developed in this field. This will also help the researchers to identify the research gaps present there. To this end, this paper looks over the approaches in the literature designed for predicting chronic diseases like Breast Cancer, Lung Cancer, Leukemia, Heart Disease, Diabetes, Chronic Kidney Disease and Liver Disease. The advantages and disadvantages of various techniques are thoroughly explained. This paper also presents a detailed performance comparison of different methods. Finally, it concludes the survey by highlighting some future research directions in this field that can be addressed through the forthcoming research attempts.
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Affiliation(s)
- Samir Malakar
- Department of Computer Science, Asutosh College, Kolkata, India
| | - Soumya Deep Roy
- Department of Metallurgical and Material Engineering, Jadavpur University, Kolkata, India
| | - Soham Das
- Department of Metallurgical and Material Engineering, Jadavpur University, Kolkata, India
| | - Swaraj Sen
- Department of Computer Science and Engineering, Jadavpur University, Kolkata, India
| | - Juan D. Velásquez
- Departament of Industrial Engineering, University of Chile, Santiago, Chile
- Instituto Sistemas Complejos de Ingeniería (ISCI), Santiago, Chile
| | - Ram Sarkar
- Department of Computer Science and Engineering, Jadavpur University, Kolkata, India
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22
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An optimized deep learning architecture for breast cancer diagnosis based on improved marine predators algorithm. Neural Comput Appl 2022; 34:18015-18033. [PMID: 35698722 PMCID: PMC9175533 DOI: 10.1007/s00521-022-07445-5] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2021] [Accepted: 05/14/2022] [Indexed: 11/12/2022]
Abstract
Breast cancer is the second leading cause of death in women; therefore, effective early detection of this cancer can reduce its mortality rate. Breast cancer detection and classification in the early phases of development may allow for optimal therapy. Convolutional neural networks (CNNs) have enhanced tumor detection and classification efficiency in medical imaging compared to traditional approaches. This paper proposes a novel classification model for breast cancer diagnosis based on a hybridized CNN and an improved optimization algorithm, along with transfer learning, to help radiologists detect abnormalities efficiently. The marine predators algorithm (MPA) is the optimization algorithm we used, and we improve it using the opposition-based learning strategy to cope with the implied weaknesses of the original MPA. The improved marine predators algorithm (IMPA) is used to find the best values for the hyperparameters of the CNN architecture. The proposed method uses a pretrained CNN model called ResNet50 (residual network). This model is hybridized with the IMPA algorithm, resulting in an architecture called IMPA-ResNet50. Our evaluation is performed on two mammographic datasets, the mammographic image analysis society (MIAS) and curated breast imaging subset of DDSM (CBIS-DDSM) datasets. The proposed model was compared with other state-of-the-art approaches. The obtained results showed that the proposed model outperforms the compared state-of-the-art approaches, which are beneficial to classification performance, achieving 98.32% accuracy, 98.56% sensitivity, and 98.68% specificity on the CBIS-DDSM dataset and 98.88% accuracy, 97.61% sensitivity, and 98.40% specificity on the MIAS dataset. To evaluate the performance of IMPA in finding the optimal values for the hyperparameters of ResNet50 architecture, it compared to four other optimization algorithms including gravitational search algorithm (GSA), Harris hawks optimization (HHO), whale optimization algorithm (WOA), and the original MPA algorithm. The counterparts algorithms are also hybrid with the ResNet50 architecture produce models named GSA-ResNet50, HHO-ResNet50, WOA-ResNet50, and MPA-ResNet50, respectively. The results indicated that the proposed IMPA-ResNet50 is achieved a better performance than other counterparts.
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23
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Baic A, Plaza D, Lange B, Michalecki Ł, Stanek A, Kowalczyk A, Ślosarek K, Cholewka A. Long-Term Skin Temperature Changes after Breast Cancer Radiotherapy. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:6891. [PMID: 35682472 PMCID: PMC9180487 DOI: 10.3390/ijerph19116891] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/30/2022] [Revised: 05/16/2022] [Accepted: 06/02/2022] [Indexed: 11/17/2022]
Abstract
The aim of the study was to use thermal imaging to evaluate long-term chest temperature changes in patients who had previously been treated with radiotherapy. The examination with a thermal imaging camera involved 144 women-48 of them were patients after RT, 48 were females before breast cancer radiotherapy and the last group of participants were 48 healthy women. All patients (before and after radiotherapy) were divided into women after mastectomy and those after conservative surgery. In addition, the first group of women, those who had received radiotherapy, were divided into three other groups: up to 1 year after RT, over 1 year and up to 5 years after RT and over 5 years after RT. Due to this, it was possible to compare the results and analyse the differences between the temperature in the healthy and treated breasts. The comparison of obtained temperature results showed that the area treated by ionizing radiation is characterized by a higher temperature even a few years after the finished treatment. It is worth mentioning that despite the fact that the difference was visible on the thermograms, the patients had no observable skin lesion or change in color at the treatment site. For the results of the study provided for the group of healthy patients, there were no significant differences observed between the average temperatures in the breasts. The use of thermal imaging in the evaluation of skin temperature changes after radiotherapy showed that the average temperature in the treated breast area can change even a long time after treatment.
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Affiliation(s)
- Agnieszka Baic
- Faculty of Science and Technology, University of Silesia, 75 Pułku Piechoty Street 1A, 41-500 Chorzów, Poland;
| | - Dominika Plaza
- Radiotherapy Planning Department, Maria Skłodowska—Curie National Research Institute of Oncology Gliwice Branch, Wybrzeze Armii Krajowej Street 15, 44-102 Gliwice, Poland; (D.P.); (K.Ś.)
| | - Barbara Lange
- IIIrd Radiotherapy and Chemotherapy Department, Maria Skłodowska—Curie National Research Institute of Oncology Gliwice Branch, Wybrzeze Armii Krajowej Street 15, 44-102 Gliwice, Poland;
| | - Łukasz Michalecki
- Department of Radiation Oncology, University Clinical Center, Medical University of Silesia, Ceglana Street 35, 40-514 Katowice, Poland;
| | - Agata Stanek
- Clinical Department of Internal Medicine, Angiology and Physical Medicine, Medical University of Silesia, Poniatowskiego Steet 15, 40-055 Katowice, Poland;
| | - Anna Kowalczyk
- Department of Physiotherapy, School of Health Sciences, Medical University of Silesia, Medyków Street 12, 40-752 Katowice, Poland;
| | - Krzysztof Ślosarek
- Radiotherapy Planning Department, Maria Skłodowska—Curie National Research Institute of Oncology Gliwice Branch, Wybrzeze Armii Krajowej Street 15, 44-102 Gliwice, Poland; (D.P.); (K.Ś.)
| | - Armand Cholewka
- Faculty of Science and Technology, University of Silesia, 75 Pułku Piechoty Street 1A, 41-500 Chorzów, Poland;
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24
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Detection of Breast Cancer from Five-View Thermal Images Using Convolutional Neural Networks. JOURNAL OF HEALTHCARE ENGINEERING 2022; 2022:4295221. [PMID: 35265301 PMCID: PMC8901325 DOI: 10.1155/2022/4295221] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/15/2021] [Revised: 01/06/2022] [Accepted: 01/19/2022] [Indexed: 11/18/2022]
Abstract
Breast cancer is one of the most common forms of cancer. Its aggressive nature coupled with high mortality rates makes this cancer life-threatening; hence early detection gives the patient a greater chance of survival. Currently, the preferred diagnosis method is mammography. However, mammography is expensive and exposes the patient to radiation. A cost-effective and less invasive method known as thermography is gaining popularity. Bearing this in mind, the work aims to initially create machine learning models based on convolutional neural networks using multiple thermal views of the breast to detect breast cancer using the Visual DMR dataset. The performances of these models are then verified with the clinical data. Findings indicate that the addition of clinical data decisions to the model helped increase its performance. After building and testing two models with different architectures, the model used the same architecture for all three views performed best. It performed with an accuracy of 85.4%, which increased to 93.8% after the clinical data decision was added. After the addition of clinical data decisions, the model was able to classify more patients correctly with a specificity of 96.7% and sensitivity of 88.9% when considering sick patients as the positive class. Currently, thermography is among the lesser-known diagnosis methods with only one public dataset. We hope our work will divert more attention to this area.
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Roslidar R, Syaryadhi M, Saddami K, Pradhan B, Arnia F, Syukri M, Munadi K. BreaCNet: A high-accuracy breast thermogram classifier based on mobile convolutional neural network. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2022; 19:1304-1331. [PMID: 35135205 DOI: 10.3934/mbe.2022060] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
The presence of a well-trained, mobile CNN model with a high accuracy rate is imperative to build a mobile-based early breast cancer detector. In this study, we propose a mobile neural network model breast cancer mobile network (BreaCNet) and its implementation framework. BreaCNet consists of an effective segmentation algorithm for breast thermograms and a classifier based on the mobile CNN model. The segmentation algorithm employing edge detection and second-order polynomial curve fitting techniques can effectively capture the thermograms' region of interest (ROI), thereby facilitating efficient feature extraction. The classifier was developed based on ShuffleNet by adding one block consisting of a convolutional layer with 1028 filters. The modified Shufflenet demonstrated a good fit learning with 6.1 million parameters and 22 MB size. Simulation results showed that modified ShuffleNet alone resulted in a 72% accuracy rate, but the performance excelled to a 100% accuracy rate when integrated with the proposed segmentation algorithm. In terms of diagnostic accuracy of the normal and abnormal test, BreaCNet significantly improves the sensitivity rate from 43% to 100% and specificity of 100%. We confirmed that feeding only the ROI of the input dataset to the network can improve the classifier's performance. On the implementation aspect of BreaCNet, the on-device inference is recommended to ensure users' data privacy and handle an unreliable network connection.
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Affiliation(s)
- Roslidar Roslidar
- Doctoral Program, School of Engineering, Universitas Syiah Kuala, Banda Aceh, Indonesia
- Department of Electrical and Computer Engineering, Universitas Syiah Kuala, Indonesia
- Telematics Research Center, Universitas Syiah Kuala, Banda Aceh, Indonesia
| | - Mohd Syaryadhi
- Department of Electrical and Computer Engineering, Universitas Syiah Kuala, Indonesia
| | - Khairun Saddami
- Department of Electrical and Computer Engineering, Universitas Syiah Kuala, Indonesia
| | - Biswajeet Pradhan
- Centre for Advanced Modelling and Geospatial Information Systems (CAMGIS), Faculty of Engineering and IT, University of Technology Sydney, Ultimo, Australia
- Center of Excellence for Climate Change Research, King Abdulaziz University, Jeddah, Saudi Arabia
- Earth Observation Center, Institute of Climate Change, Universiti Kebangsaan Malaysia, Bangi, Malaysia
| | - Fitri Arnia
- Department of Electrical and Computer Engineering, Universitas Syiah Kuala, Indonesia
- Telematics Research Center, Universitas Syiah Kuala, Banda Aceh, Indonesia
| | - Maimun Syukri
- Medical Faculty, Universitas Syiah Kuala, Banda Aceh, Indonesia
| | - Khairul Munadi
- Department of Electrical and Computer Engineering, Universitas Syiah Kuala, Indonesia
- Telematics Research Center, Universitas Syiah Kuala, Banda Aceh, Indonesia
- Tsunami and Disaster Mitigation Research Center, Universitas Syiah Kuala, Banda Aceh, Indonesia
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