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It is one of the significant reasons among ladies, regardless of huge endeavors to stay away from it through screening developers. An automatic detection system for disease helps doctors to identify and provide accurate results, thereby minimizing the death rate. Computer\u2010aided diagnosis (CAD) has minimum intervention of humans and produces more accurate results than humans. It will be a difficult and long task that depends on the expertise of pathologists. Deep learning methods proved to give better outcomes when correlated with ML and extricate the best highlights of the images. The main objective of this paper is to propose a deep learning technique in combination with a convolution neural network (CNN) and long short\u2010term memory (LSTM) with a random forest algorithm to diagnose breast cancer. Here, CNN is used for feature extraction, and LSTM is used for extracted feature detection. The experimental results show that the proposed system accomplishes 100% of accuracy, a sensitivity of 99%, recall of 99%, and an F1\u2010score of 98% compared to other traditional models. As the system achieved correct results, it can help doctors to investigate breast cancer easily.<\/jats:p>","DOI":"10.1155\/2022\/9299621","type":"journal-article","created":{"date-parts":[[2022,9,10]],"date-time":"2022-09-10T16:20:07Z","timestamp":1662826807000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":58,"title":["A Combined Deep CNN: LSTM with a Random Forest Approach for Breast Cancer Diagnosis"],"prefix":"10.1155","volume":"2022","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0955-7185","authenticated-orcid":false,"given":"Almas","family":"Begum","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2274-8127","authenticated-orcid":false,"given":"V.","family":"Dhilip Kumar","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2218-0789","authenticated-orcid":false,"given":"Junaid","family":"Asghar","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2667-5140","authenticated-orcid":false,"given":"D.","family":"Hemalatha","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5166-3037","authenticated-orcid":false,"given":"G.","family":"Arulkumaran","sequence":"additional","affiliation":[]}],"member":"311","published-online":{"date-parts":[[2022,9,10]]},"reference":[{"key":"e_1_2_7_1_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.fcij.2017.12.001"},{"key":"e_1_2_7_2_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-44289-7_30"},{"key":"e_1_2_7_3_2","article-title":"Handwritten digit recognition with a back-propagation network","volume":"2","author":"LeCun Y.","year":"1989","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_2_7_4_2","doi-asserted-by":"publisher","DOI":"10.1097\/rli.0000000000000358"},{"key":"e_1_2_7_5_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2016.07.007"},{"key":"e_1_2_7_6_2","doi-asserted-by":"crossref","unstructured":"DhungelN. 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