Advanced and futuristic approaches for breast cancer diagnosis

Mishra, Jayanti; Kumar, Bhumika; Targhotra, Monika; Sahoo, PK

doi:10.1186/s43094-020-00113-2

Cited by 3 publications

(2 citation statements)

References 74 publications

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“…That system will helpful in future for early diagnose, and improve the patient survival and save the life. Some futuristic approach has given that will used to improve the detection approach like breath biopsy that helps to analyze breathe sample to stratify bio markers, mammary ductoscopy is a procedure that involves inserting a tiny endoscope into the breast milk ducts to enable for observation of the ductal epithelial lining and cell recovery [51]. A review on digital image-based shows AI technologies for diagnose of skin cancer [52].…”

Section: 𝑅𝐸𝐶𝐴𝐿𝐿 = 𝑇𝑃 𝑇𝑃+𝐹𝑁mentioning

confidence: 99%

A Comparative Analysis of Machine Learning Algorithms for Breast Cancer Detection and Identification of Key Predictive Features

Kumar,

Saini,

Kumar

2024

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Cancer, a disease with numerous subtypes, poses a deadly threat to human life, with the potential for successful clinical treatment heavily reliant on early detection and appropriate treatment planning. The classification of cancer patients into either low or high-risk subgroups is critical. Consequently, various research teams spanning the biomedical and bioinformatics fields have explored the use of Machine Learning (ML) technology in this crucial domain. The impressive capability of ML algorithms to discern significant features in complex datasets underscores their value. In the current study, we propose a framework to detect breast cancer (through benign and malignant categorization) utilizing advanced ML techniques with high accuracy. This framework deploys the Wisconsin Breast Cancer (Diagnostic) dataset. Five supervised ML techniques, namely Decision Tree, Random Forest (RF), Support Vector Machine (SVM), Extreme Gradient Boosting (XGBoost), and Artificial Neural Network (ANN), are trained for classification purposes. Out of 569 samples, 70% are allocated for training while the other 30% for testing. A comprehensive evaluation of ML techniques is performed using an array of metrics: precision, recall, specificity, F1 score, classification accuracy, ROC Curve, training time, and feature utilization. Additionally, feature importance is computed for each classifier. The results reveal that the SVM has the maximum accuracy as 97.66%, with an F1-score of 0.98 for benign and 0.97 for malignant classifications. Conversely, the decision tree registers the minimum performance (94.55%) with an F1-score of 0.95 for benign and 0.91 for malignant classes. Accuracy scores for RF, XGBoost, and ANN stand at 95.32%, 95.91%, and 97.07%, with corresponding F1-scores of 0.96, 0.97, and 0.98 for benign and 0.94, 0.95, and 0.96 for malignant respectively. Interestingly, RF and XGBoost exhibited near-equivalent similarly with respect of accuracy measurements. In the context of the area over the ROC curve, SVM outperformed the other ML classifiers and also reported the shortest training time. Conversely, the ANN reported the longest training time.

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Section: 𝑅𝐸𝐶𝐴𝐿𝐿 = 𝑇𝑃 𝑇𝑃+𝐹𝑁mentioning

confidence: 99%

A Comparative Analysis of Machine Learning Algorithms for Breast Cancer Detection and Identification of Key Predictive Features

Kumar,

Saini,

Kumar

2024

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“…Its rising trend may be attributable to various environmental and genetic factors [ 2 ]. However, improved diagnostic techniques and awareness have led to early detection of this disease [ 3 , 4 ] and, subsequently, the increase in the number of diagnosed cases. The alarming rise in the number of breast cancer cases has led to advancements in diagnostic techniques, as early detection has a better prognosis than late detection [ 5 ].…”

Section: Introductionmentioning

confidence: 99%

Can Artificial Intelligence Beat Humans in Detecting Breast Malignancy on Mammograms?

Malik,

Yasmin,

Kumar

et al. 2023

Cureus

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Background: The study was aimed at identifying how useful Computer-Aided Detection (CAD) could be in reducing false-negative reporting in mammography and early detection of breast cancer at an early stage as the best protection is early detection. Materials and methods: This retrospective study was conducted in a tertiary care setup of Atomic Energy Cancer Hospital, Nuclear Medicine, Oncology and Radiotherapy Institute (AECH-NORI), where 33 patients with suspicious findings on mammography and subsequent biopsy-proven malignancy were included. The findings of mammography including the lesion type, breast parenchymal density, and sensitivity of CAD detection, as well as the final biopsy results, were recorded. A second group of 40 normal screening mammograms was also included who had no symptoms, had Breast Imaging-Reporting and Data System category I(BI-RADS I) mammograms, and had no pathology identified on correlative sonomammography as well. Results: A total of 35 masses, 11 pleomorphic clusters of microcalcification, five clustered foci of macrocalcification, and nine lesions with pleomorphic clusters of microcalcification and two with pleomorphic clusters of microcalcification only were included. The CAD system was able to identify 26 masses (74%), eight lesions with pleomorphic clusters of microcalcification (72%), five foci of macrocalcification (100%), six lesions with pleomorphic clusters of microcalcification (66%), and two pleomorphic clusters of microcalcification without formed mass (100%). The overall sensitivity of the CAD system was 75.8%. CAD was able to identify 13 out of 16 masses with invasive ductal carcinoma (81.3%), eight out of nine lesions proven as invasive ductal carcinoma with ductal carcinoma in situ (DCIS) (88.9%), two out of five masses with invasive lobular carcinoma (40%), four out of four masses with invasive mammary carcinoma (100%), and zero out of one lesion identified as medullary carcinoma (0%). There was 100% detection for pleomorphic clusters of microcalcification without formed mass with CAD marking two out of two mammograms. Conclusion: CAD performed better with combined lesions, accurately marked pleomorphic clusters of microcalcification, and identified small lesions in predominant fibrofatty parenchymal density but was not reliable in dense breast, areas of asymmetric increased density, summation artifacts, edematous breast parenchyma, and retroareolar lesions. It also performed poorly with ill-defined lesions of invasive lobular carcinoma. Human intelligence hence beats CAD for the diagnosis of breast malignancy in mammograms as per our experience.

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