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Radiologists’ diagnostic reliability can increase when computer-aided systems precisely locate breast tumors from noisy mammographic images. Using the MIAS dataset, this study evaluates noise reduction and identifies salt-and-pepper, Gaussian, and Poisson noise to enable precise mass detection. A hybrid QWT-APCNN approach combines quantum wavelet transform filtering with an Atrous pyramid convolutional neural network for segmentation and classification, achieving strong performance across PSNR and MSE, and reporting metrics such as 98.57% accuracy, 92% sensitivity, and 88% specificity.",{"@graph":69,"@context":123},[70,84,106],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":35,"@type":76,"position":81},"https://docshare.wps.com/document/healthcare/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/employing-atrous-pyramid-convolutional-deep-learning-approach-for-detection-to-diagnose-breast-cancer-tumors/381853/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/employing-atrous-pyramid-convolutional-deep-learning-approach-for-detection-to-diagnose-breast-cancer-tumors/381853.png","ImageObject",300,407,{"name":92,"@type":93},"Franzy","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-27","2026-09-24",true,{"@type":102,"interactionType":103,"userInteractionCount":105},"InteractionCounter",{"@type":104},"ViewAction",11,{"@type":107,"mainEntity":108},"FAQPage",[109,115,119],{"name":110,"@type":111,"acceptedAnswer":112},"What dataset is used for the breast cancer diagnosis task?","Question",{"text":113,"@type":114},"The study uses the MIAS dataset as the input for mammography images and associated features.","Answer",{"name":116,"@type":111,"acceptedAnswer":117},"How does the proposed method reduce noise in mammographic images?",{"text":118,"@type":114},"It applies a quantum wavelet transform (QWT) filtering algorithm to reduce noise before further morphological processing.",{"name":120,"@type":111,"acceptedAnswer":121},"What performance indicators are used to evaluate detection and segmentation quality?",{"text":122,"@type":114},"The method is compared using peak signal-to-noise ratio (PSNR) and mean square error (MSE), alongside reported accuracy-related measures such as sensitivity, specificity, DSS, and ROC/AUC.","https://schema.org",{"og:url":83,"og:type":125,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":127,"canonical":83},"index,follow",{"doc_id":129,"site_id":62},381853,1790746566,{"code":4,"msg":5,"data":132},{"doc_id":129,"user_id":133,"nickname":92,"user_avatar":134,"doc_module":4,"category_id":34,"category_name":35,"doc_title":65,"doc_description":67,"doc_content":135,"file_id":136,"file_url":137,"file_type":138,"file_size":139,"view_count":105,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":140,"language":141,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":142,"faqs":143,"seo_title":144,"seo_description":67,"update_tm":145,"read_time":146},2336478945519,"https://ap-avatar.wpscdn.com/davatar_085a072bc5b1113ac321206ff7593b45","Hindawi  \nComputational Intelligence and Neuroscience Volume 2023, Article ID 7201479, 17 pages [https://doi.org/10.1155/2023/7201479](https://doi.org/10.1155/2023/7201479)  \nResearch Article  \nEmploying Atrous Pyramid Convolutional Deep Learning Approach for Detection to Diagnose Breast Cancer Tumors  \nEhsan Sadeghi Pour, Mahdi Esmaeili , and Morteza Romoozi  \nDepartment of Electrical and Computer Engineering, Kashan Branch, Islamic Azad University, Kashan 8715998151, Iran Correspondence should be addressed to Mahdi Esmaeili; [m.esmaeili@iaukashan.ac.ir](m.esmaeili@iaukashan.ac.ir)  \nReceived 20 May 2022; Revised 8 October 2022; Accepted 24 November 2022; Published 14 November 2023  \nAcademic Editor: Mohammed A. A. Al qaness  \nCopyright © 2023 Ehsan Sadeghi Pour et al. Tis is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.  \nBreast cancer is among the most common diseases and one of the most common causes of death in the female population worldwide. Early identifcation of breast cancer improves survival. Terefore, radiologists will be able to make more accurate diagnoses ifa computerized system is developed to detect breast cancer. Computer-aided design techniques have the potential to help medical professionals to determine the specifc location of breast tumors and better manage this disease more rapidly and accurately. MIAS datasets were used in this study. Te aim of this study is to evaluate a noise reduction for mammographic pictures and to identify salt and pepper, Gaussian, and Poisson so that precise mass detection operations can be estimated. Asa result, it provides a method for noise reduction known as quantum wavelet transform (QWT) fltering and an image morphology operator for precise mass segmentation in mammographic images by utilizing an Atrous pyramid convolutional neural network as the deep learning model for classifcation of mammographic images. Te hybrid methodology dubbed QWT-APCNN is compared to earlier methods in terms of peak signal-to-noise ratio (PSNR) and mean square error (MSE) in noise reduction and detection accuracy for mass area recognition. Compared to state-of-the-art approaches, the proposed method performed better at noise reduction and segmentation according to diferent evaluation criteria such as an accuracy rate of 98.57%, 92% sensitivity, 88% specifcity, 90% DSS, and ROC and AUC rate of 88.77 .  \n1. Introduction  \nBreast cancer occurs in the breast and has symptoms such asa lump in the breast, breast appearance changes, breast skin dimpling, nipple discharge other than breast milk, and/or faky skin. Breast cancer is the second-most frequent cancer among women and causes a large number of deaths every year. It was reported that breast cancer is almost impossible to prevent since its causes remain unknown [1] . Terefore, early diagnosis is crucial in the treatment of breast cancer. Mammography is widely used by radiologists to diagnose and screen breast cancer. Today, mammography is the most commonly used technique for the early diagnosis of breast cancer and has reportedly lowered the mortality rate to 25% . However, it is difcult to interpret and describe mammographic images [2] . To obtain more accurate results, image preprocessing is required [1] . Preprocessing is primarily  \ncarried out to enhance image quality and improve diagnosis by removing unimportant segments from the background and to precisely extract breast areas by revealing breast boundaries [2] . Te current mammography is based on smart medical diagnosis systems with image processing using machine learning (ML) . Image processing principles in smart medical systems are important for the diagnosis of breast cancer since mammographic pictures are intrinsically noisy, which may challenge the diagnosis. In reference [3], a number of optimal flters have been introduced in","cbCaisTp2INsBc5Z","https://ap.wps.com/l/cbCaisTp2INsBc5Z","pdf",1333435,17,"English","# Introduction\n## Proposed Approach Overview\n## Noise Reduction and Mass Segmentation\n## Atrous Pyramid CNN-Based Classification","[{\"question\":\"What dataset is used for the breast cancer diagnosis task?\",\"answer\":\"The study uses the MIAS dataset as the input for mammography images and associated features.\"},{\"question\":\"How does the proposed method reduce noise in mammographic images?\",\"answer\":\"It applies a quantum wavelet transform (QWT) filtering algorithm to reduce noise before further morphological processing.\"},{\"question\":\"What performance indicators are used to evaluate detection and segmentation quality?\",\"answer\":\"The method is compared using peak signal-to-noise ratio (PSNR) and mean square error (MSE), alongside reported accuracy-related measures such as sensitivity, specificity, DSS, and ROC/AUC.\"}]","Employing Atrous Pyramid Convolutional Deep Learning Approach for Detection to Diagnose Breast Cancer Tumors | PDF",1790247470,43]