{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,30]],"date-time":"2026-01-30T00:43:37Z","timestamp":1769733817879,"version":"3.49.0"},"reference-count":28,"publisher":"Springer Science and Business Media LLC","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,7,4]]},"DOI":"10.7759\/cureus.87301","type":"journal-article","created":{"date-parts":[[2025,7,5]],"date-time":"2025-07-05T02:18:27Z","timestamp":1751681907000},"source":"Crossref","is-referenced-by-count":1,"title":["A Pilot Study of Breast Cancer Histopathological Image Classification Using Google Teachable Machine: A No-Code Artificial Intelligence Approach"],"prefix":"10.7759","author":[{"given":"Namit","family":"Singla","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Abhra","family":"Ghosh","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Manthan","family":"Dhingra","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Upajna","family":"Pal","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Arkajit","family":"Dasgupta","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Arpan","family":"Ghosh","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tae-Yong","family":"Kuc","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Krisha","family":"Singla","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1186\/s40659-017-0140-9","article-title":"Awareness and current knowledge of breast cancer","volume":"50","author":"Akram M","year":"2017","unstructured":"Akram M, Iqbal M, Daniyal M, Khan AU. Awareness and current knowledge of breast cancer. Biol Res. 2017, 50:33. 10.1186\/s40659-017-0140-9","journal-title":"Biol Res"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.3322\/caac.21660","article-title":"Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries","volume":"71","author":"Sung H","year":"2021","unstructured":"Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, Bray F. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2021, 71:209-49. 10.3322\/caac.21660","journal-title":"CA Cancer J Clin"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.3390\/diagnostics13193113","article-title":"Breast cancer detection and prevention using machine learning","volume":"13","author":"Khalid A","year":"2023","unstructured":"Khalid A, Mehmood A, Alabrah A, Alkhamees BF, Amin F, AlSalman H, Choi GS. Breast cancer detection and prevention using machine learning. Diagnostics (Basel). 2023, 13:10.3390\/diagnostics13193113","journal-title":"Diagnostics (Basel)"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1016\/0046-8177(95)90010-1","article-title":"Histological grading of breast carcinomas: a study of interobserver agreement","volume":"26","author":"Robbins P","year":"1995","unstructured":"Robbins P, Pinder S, de Klerk N, et al.. Histological grading of breast carcinomas: a study of interobserver agreement. Hum Pathol. 1995, 26:873-9. 10.1016\/0046-8177(95)90010-1","journal-title":"Hum Pathol"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1080\/14737159.2024.2346545","article-title":"Artificial intelligence in digital histopathology for predicting patient prognosis and treatment efficacy in breast cancer","volume":"24","author":"McCaffrey C","year":"2024","unstructured":"McCaffrey C, Jahangir C, Murphy C, Burke C, Gallagher WM, Rahman A. Artificial intelligence in digital histopathology for predicting patient prognosis and treatment efficacy in breast cancer. Expert Rev Mol Diagn. 2024, 24:363-77. 10.1080\/14737159.2024.2346545","journal-title":"Expert Rev Mol Diagn"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.3233\/CBM-230251","article-title":"Deep learning approaches for breast cancer detection in histopathology images: a review","volume":"40","author":"Priya L","year":"2024","unstructured":"Priya L, V G B, B R V, Ramachandran S. Deep learning approaches for breast cancer detection in histopathology images: a review. Cancer Biomark. 2024, 40:1-25. 10.3233\/CBM-230251","journal-title":"Cancer Biomark"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.22034\/APJCP.2017.18.12.3251","article-title":"Inter- and intra-observer variability in diagnosis of oral dysplasia","volume":"18","author":"R SA","year":"2017","unstructured":"R SA, B N P, Hegde U, K U, G S, G K, Sil S. Inter- and intra-observer variability in diagnosis of oral dysplasia. Asian Pac J Cancer Prev. 2017, 18:3251-4. 10.22034\/APJCP.2017.18.12.3251","journal-title":"Asian Pac J Cancer Prev"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1186\/bcr2607","article-title":"Breast cancer prognostic classification in the molecular era: the role of histological grade","volume":"12","author":"Rakha EA","year":"2010","unstructured":"Rakha EA, Reis-Filho JS, Baehner F, et al.. Breast cancer prognostic classification in the molecular era: the role of histological grade. Breast Cancer Res. 2010, 12:207. 10.1186\/bcr2607","journal-title":"Breast Cancer Res"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1038\/s41523-022-00478-y","article-title":"Deep learning models for histologic grading of breast cancer and association with disease prognosis","volume":"8","author":"Jaroensri R","year":"2022","unstructured":"Jaroensri R, Wulczyn E, Hegde N, et al.. Deep learning models for histologic grading of breast cancer and association with disease prognosis. NPJ Breast Cancer. 2022, 8:113. 10.1038\/s41523-022-00478-y","journal-title":"NPJ Breast Cancer"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1186\/s13000-020-00995-z","article-title":"Accuracy and efficiency of an artificial intelligence tool when counting breast mitoses","volume":"15","author":"Pantanowitz L","year":"2020","unstructured":"Pantanowitz L, Hartman D, Qi Y, et al.. Accuracy and efficiency of an artificial intelligence tool when counting breast mitoses. Diagn Pathol. 2020, 15:80. 10.1186\/s13000-020-00995-z","journal-title":"Diagn Pathol"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1016\/j.breast.2019.12.007","article-title":"Artificial intelligence in digital breast pathology: techniques and applications","volume":"49","author":"Ibrahim A","year":"2020","unstructured":"Ibrahim A, Gamble P, Jaroensri R, Abdelsamea MM, Mermel CH, Chen PC, Rakha EA. Artificial intelligence in digital breast pathology: techniques and applications. Breast. 2020, 49:267-73. 10.1016\/j.breast.2019.12.007","journal-title":"Breast"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1186\/s13000-024-01453-w","article-title":"Artificial intelligence's impact on breast cancer pathology: a literature review","volume":"19","author":"Soliman A","year":"2024","unstructured":"Soliman A, Li Z, Parwani AV. Artificial intelligence's impact on breast cancer pathology: a literature review. Diagn Pathol. 2024, 19:38. 10.1186\/s13000-024-01453-w","journal-title":"Diagn Pathol"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.3390\/cancers14092243","article-title":"Development of an image analysis-based prognosis score using Google\u2019s Teachable Machine in melanoma","volume":"14","author":"Forchhammer S","year":"2022","unstructured":"Forchhammer S, Abu-Ghazaleh A, Metzler G, Garbe C, Eigentler T. Development of an image analysis-based prognosis score using Google\u2019s Teachable Machine in melanoma. Cancers (Basel). 2022, 14:10.3390\/cancers14092243","journal-title":"Cancers (Basel)"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/HIBIT.2010.5478893","article-title":"Microarray data analysis for cancer classification","volume":"20","author":"Osareh A","year":"2010","unstructured":"Osareh A, Shadgar B. Microarray data analysis for cancer classification. Int Symp Health Inform Bioinform. 2010, 20:125-32. 10.1109\/HIBIT.2010.5478893","journal-title":"Int Symp Health Inform Bioinform"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.3390\/jcm9030749","article-title":"Artificial intelligence-based mitosis detection in breast cancer histopathology images using faster R-CNN and deep CNNs","volume":"9","author":"Mahmood T","year":"2020","unstructured":"Mahmood T, Arsalan M, Owais M, Lee MB, Park KR. Artificial intelligence-based mitosis detection in breast cancer histopathology images using faster R-CNN and deep CNNs. J Clin Med. 2020, 9:10.3390\/jcm9030749","journal-title":"J Clin Med"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1016\/j.jpi.2023.100316","article-title":"Deep learning supported mitoses counting on whole slide images: a pilot study for validating breast cancer grading in the clinical workflow","volume":"14","author":"van Bergeijk SA","year":"2023","unstructured":"van Bergeijk SA, Stathonikos N, Ter Hoeve ND, Lafarge MW, Nguyen TQ, van Diest PJ, Veta M. Deep learning supported mitoses counting on whole slide images: a pilot study for validating breast cancer grading in the clinical workflow. J Pathol Inform. 2023, 14:100316. 10.1016\/j.jpi.2023.100316","journal-title":"J Pathol Inform"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1038\/s41374-020-00514-0","article-title":"Artificial intelligence and computational pathology","volume":"101","author":"Cui M","year":"2021","unstructured":"Cui M, Zhang DY. Artificial intelligence and computational pathology. Lab Invest. 2021, 101:412-22. 10.1038\/s41374-020-00514-0","journal-title":"Lab Invest"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1038\/s41746-020-0221-y","article-title":"An overview of clinical decision support systems: benefits, risks, and strategies for success","volume":"3","author":"Sutton RT","year":"2020","unstructured":"Sutton RT, Pincock D, Baumgart DC, Sadowski DC, Fedorak RN, Kroeker KI. An overview of clinical decision support systems: benefits, risks, and strategies for success. NPJ Digit Med. 2020, 3:17. 10.1038\/s41746-020-0221-y","journal-title":"NPJ Digit Med"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1016\/j.artmed.2020.101845","article-title":"Breast cancer diagnosis from histopathological images using textural features and CBIR","volume":"105","author":"Carvalho ED","year":"2020","unstructured":"Carvalho ED, Filho AO, Silva RR, et al.. Breast cancer diagnosis from histopathological images using textural features and CBIR. Artif Intell Med. 2020, 105:101845. 10.1016\/j.artmed.2020.101845","journal-title":"Artif Intell Med"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1038\/srep46450","article-title":"Accurate and reproducible invasive breast cancer detection in whole-slide images: a Deep Learning approach for quantifying tumor extent","volume":"7","author":"Cruz-Roa A","year":"2017","unstructured":"Cruz-Roa A, Gilmore H, Basavanhally A, et al.. Accurate and reproducible invasive breast cancer detection in whole-slide images: a Deep Learning approach for quantifying tumor extent. Sci Rep. 2017, 7:46450. 10.1038\/srep46450","journal-title":"Sci Rep"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1136\/jclinpath-2017-204644","article-title":"Future-proofing pathology: the case for clinical adoption of digital pathology","volume":"70","author":"Williams BJ","year":"2017","unstructured":"Williams BJ, Bottoms D, Treanor D. Future-proofing pathology: the case for clinical adoption of digital pathology. J Clin Pathol. 2017, 70:1010-8. 10.1136\/jclinpath-2017-204644","journal-title":"J Clin Pathol"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.1717139115","article-title":"Predicting cancer outcomes from histology and genomics using convolutional networks","volume":"115","author":"Mobadersany P","year":"2018","unstructured":"Mobadersany P, Yousefi S, Amgad M, et al.. Predicting cancer outcomes from histology and genomics using convolutional networks. Proc Natl Acad Sci U S A. 2018, 115:E2970-9. 10.1073\/pnas.1717139115","journal-title":"Proc Natl Acad Sci U S A"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.3390\/jpm12091496","article-title":"Application of artificial intelligence techniques to predict risk of recurrence of breast cancer: a systematic review","volume":"12","author":"Mazo C","year":"2022","unstructured":"Mazo C, Aura C, Rahman A, Gallagher WM, Mooney C. Application of artificial intelligence techniques to predict risk of recurrence of breast cancer: a systematic review. J Pers Med. 2022, 12:10.3390\/jpm12091496","journal-title":"J Pers Med"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1038\/modpathol.2016.109","article-title":"Standardized evaluation of tumor-infiltrating lymphocytes in breast cancer: results of the ring studies of the international immuno-oncology biomarker working group","volume":"29","author":"Denkert C","year":"2016","unstructured":"Denkert C, Wienert S, Poterie A, et al.. Standardized evaluation of tumor-infiltrating lymphocytes in breast cancer: results of the ring studies of the international immuno-oncology biomarker working group. Mod Pathol. 2016, 29:1155-64. 10.1038\/modpathol.2016.109","journal-title":"Mod Pathol"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1158\/1055-9965.EPI-09-1023","article-title":"Assessment of automated image analysis of breast cancer tissue microarrays for epidemiologic studies","volume":"19","author":"Bolton KL","year":"2010","unstructured":"Bolton KL, Garcia-Closas M, Pfeiffer RM, et al.. Assessment of automated image analysis of breast cancer tissue microarrays for epidemiologic studies. Cancer Epidemiol Biomarkers Prev. 2010, 19:992-9. 10.1158\/1055-9965.EPI-09-1023","journal-title":"Cancer Epidemiol Biomarkers Prev"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.5281\/zenodo.3632035","article-title":"BACH dataset: grand challenge on breast cancer histology images","volume":"56","author":"Pol\u00f3nia A","year":"2019","unstructured":"Pol\u00f3nia A, Eloy C, Aguiar P. BACH dataset: grand challenge on breast cancer histology images. Med Image Anal. 2019, 56:122-39. 10.5281\/zenodo.3632035","journal-title":"Med Image Anal"},{"key":"ref27","unstructured":"Google Teachable Machine. (2019). Accessed. June 06, 2025: https:\/\/teachablemachine.withgoogle.com\/."},{"key":"ref28","unstructured":"Welcome to Colab!. (2017). Accessed. June 06, 2025: https:\/\/colab.research.google.com\/."}],"container-title":["Cureus"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.cureus.com\/articles\/383469-a-pilot-study-of-breast-cancer-histopathological-image-classification-using-google-teachable-machine-a-no-code-artificial-intelligence-approach","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,7,5]],"date-time":"2025-07-05T02:18:29Z","timestamp":1751681909000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.cureus.com\/articles\/383469-a-pilot-study-of-breast-cancer-histopathological-image-classification-using-google-teachable-machine-a-no-code-artificial-intelligence-approach"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,7,4]]},"references-count":28,"URL":"https:\/\/doi.org\/10.7759\/cureus.87301","relation":{},"ISSN":["2168-8184"],"issn-type":[{"value":"2168-8184","type":"print"}],"subject":[],"published":{"date-parts":[[2025,7,4]]}}}