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This study proposes a lightweight deep learning approach using thermographic images and a compact smartphone-deployable model. Edge detection methods are evaluated to enhance accuracy, and a MobileNetV2 model with spatial attention is compared with alternatives, achieving 98.88% accuracy and outperforming state-of-the-art methods.",{"@graph":69,"@context":122},[70,84,105],{"@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/lightweight-deep-learning-model-with-spatial-attention-for-accurate-and-efficient-breast-cancer-prediction/346346/",{"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/lightweight-deep-learning-model-with-spatial-attention-for-accurate-and-efficient-breast-cancer-prediction/346346.png","ImageObject",300,407,{"name":92,"@type":93},"Himbo","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-27","2026-09-22",true,{"@type":102,"interactionType":103,"userInteractionCount":19},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"Why is early breast cancer detection important?","Question",{"text":112,"@type":113},"Early detection supports more effective treatment choices and improves the likelihood of positive patient outcomes. 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It compared results against other deep learning networks like Inception ResNet and DenseNet121.","https://schema.org",{"og:url":83,"og:type":124,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":126,"canonical":83},"index,follow",{"doc_id":128,"site_id":62},346346,1790694144,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":34,"category_name":35,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":19,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":139,"language":140,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":67,"update_tm":144,"read_time":145},687197100911,"https://ap-avatar.wpscdn.com/avatar/a000239b6f1da00475?x-image-process=image/resize,m_fixed,w_180,h_180&k=1785132997149421697","[www. nature.com/scientificreports](www. nature.com/scientificreports)  \nOPEN  \nLightweight deep learning model with spatial attention for accurate and efficient breast cancer prediction  \nJaafar Jaafari1, Hind Ezzine2, Khadija Douzi1 & Samira Douzi3􀀍  \nBreast cancer is a worldwide health crisis that affects a large number of women. Early detection of this disease is critical for determining effective treatment options and improving the chances of positive patient outcomes. In our study, we introduce a new method for detecting breast cancer in its earliest stages using thermographic images and a compact model that can be easily implemented on smartphones. This method is especially useful in areas where medical resources are scarce. We used multiple edge detection techniques like Canny, Roberts and Sobel, and evaluated their effectiveness to improve the accuracy of our model. Our model, which combines MobileNetV2 with a spatial attention mechanism, outperformed other deep learning networks like Inception ResNet and DenseNet121 . Furthermore, with an accuracy rate of 98.88%, our proposed model outperformed current state-of-theart algorithms. These findings point to the potential of our approach for early breast cancer detection and its practical application in resource-limited settings.  \nAccording to the American Cancer Society, one in eight American women will be diagnosed with breast cancer at some point in their lives, resulting in over 40,000 deaths annually1. Regular breast screenings are essential for detecting breast cancer in its earliest stages, as this can significantly increase the 5-year survival rate to 99%2. Not only does early detection reduce mortality, but it also reduces morbidity and, in some cases, allows patients to avoid surgery3. When the tumor volume is less than 10 mm, there is an 85% chance of complete healing, according to research4. These statistics demonstrate conclusively the significance of early detection in enhancing the prognosis of breast cancer patients.  \nThere are numerous subtypes of breast cancer, each with its own distinct characteristics, size, and behavior. By accurately identifying the type of cancer a patient has, physicians can devise the most effective treatment plan for each individual. Invasive and non-invasive breast cancers are the two primary classifications.  \nInvasive breast cancer: also known as infiltrating breast cancer, is caused by the spread of cancer cells from the milk ducts or lobules to the surrounding breast tissue. These cancer cells can also spread via the bloodstream or lymphatic system to other organs.  \nThere are several subtypes of invasive breast cancer based on the location of their growth:  \n• Invasive ductal carcinoma (IDC) is the most prevalent form of breast cancer, accounting for roughly 80% of all cases. It develops when abnormal cells in the lining of the milk ducts transform into cancerous cells and invade the surrounding breast tissue.  \n• Invasive lobular carcinoma (ILC) is the second most prevalent form of invasive breast cancer, accounting for ten to fifteen% of all cases. It begins in the milk-producing glands of the lobules and spreads to other areas of the breast.  \nNoninvasive breast cancer: also known as in situ breast cancer, is characterized by the presence of cancer cells within the breast tissue without metastasis to lymph nodes or surrounding tissue. It should be noted, however, that this type of cancer may progress to an invasive form over time. Noninvasive breast cancer, like invasive breast cancer, has two principal subtypes: Ductal Carcinoma In Situ (DCIS) and Lobular Carcinoma In Situ (LCIS) .  \n• DCIS, also known as intraductal carcinoma, affects the cells of the breast’s milk ducts. These cells become cancerous but do not spread to the lymph nodes or bloodstream; they remain confined to the duct walls. This  \n1FSTM, University Hassan II, Casablanca, Morocco. 2Mohammed V University in Rabat, FSR, IPSS, Rabat, Morocco.  \n3Mohamme","cbCaifNnmkl6eRgX","https://ap.wps.com/l/cbCaifNnmkl6eRgX","pdf",2781331,18,"English","# Introduction\n# Breast cancer types and subtypes\n## Invasive breast cancer\n## Noninvasive breast cancer\n# Diagnostic procedures\n## Mammography\n## Ultrasound\n## Breast MRI","[{\"question\":\"Why is early breast cancer detection important?\",\"answer\":\"Early detection supports more effective treatment choices and improves the likelihood of positive patient outcomes. It also increases survival rates and can reduce mortality and, in some cases, morbidity.\"},{\"question\":\"What model does the study propose for breast cancer prediction?\",\"answer\":\"The study proposes a lightweight method combining MobileNetV2 with a spatial attention mechanism and using thermographic images. The goal is accurate prediction with efficient implementation, including on smartphones.\"},{\"question\":\"How did the study evaluate and improve accuracy?\",\"answer\":\"The study used multiple edge detection techniques such as Canny, Roberts, and Sobel, then assessed how these choices affected model performance. It compared results against other deep learning networks like Inception ResNet and DenseNet121.\"}]","Lightweight deep learning model with spatial attention for accurate and efficient breast cancer prediction | PDF",1790060992,45]