A.H.M. Zadidul Karim , Kazi Bil Oual Mahmud , Celia Shahnaz
{"title":"基于混合特征提取的集成模型用于不同医学图像的乳腺癌检测与分类","authors":"A.H.M. Zadidul Karim , Kazi Bil Oual Mahmud , Celia Shahnaz","doi":"10.1016/j.ibmed.2025.100290","DOIUrl":null,"url":null,"abstract":"<div><div>Breast cancer is a fatal disease that has a high death rate worldwide, according to the WHO. Hence, implementing medical image-based automated breast cancer detection and classification is essential for early identification and categorization. It plays a crucial role in developing efficient treatment methods by accurately diagnosing the kind and classifying the subtype of breast cancer. Ultrasound and mammograms are primary and efficient methods for detection, whereas histopathology is an advanced method for exactly classifying breast cancer. Previously, different hand-engineered features were used for different types of data sets, respectively, which provided high accuracy individually. However, deep learning is a strong tool for computer vision tasks. Therefore, we developed a unique combination of hand-engineered features for color, shape, and texture extraction in parallel to three different deep neural networks. Such a hybrid method proposed that combines both hand-engineered and deep learning-based feature extractors provides an outstanding performance for breast cancer detection and classification on different types of datasets compared to the state of the art methods thus verifying its robustness and effectiveness.</div></div>","PeriodicalId":73399,"journal":{"name":"Intelligence-based medicine","volume":"12 ","pages":"Article 100290"},"PeriodicalIF":0.0000,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A hybrid feature extraction based ensemble model for breast cancer detection and classification using different medical images\",\"authors\":\"A.H.M. Zadidul Karim , Kazi Bil Oual Mahmud , Celia Shahnaz\",\"doi\":\"10.1016/j.ibmed.2025.100290\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>Breast cancer is a fatal disease that has a high death rate worldwide, according to the WHO. Hence, implementing medical image-based automated breast cancer detection and classification is essential for early identification and categorization. It plays a crucial role in developing efficient treatment methods by accurately diagnosing the kind and classifying the subtype of breast cancer. Ultrasound and mammograms are primary and efficient methods for detection, whereas histopathology is an advanced method for exactly classifying breast cancer. Previously, different hand-engineered features were used for different types of data sets, respectively, which provided high accuracy individually. However, deep learning is a strong tool for computer vision tasks. Therefore, we developed a unique combination of hand-engineered features for color, shape, and texture extraction in parallel to three different deep neural networks. Such a hybrid method proposed that combines both hand-engineered and deep learning-based feature extractors provides an outstanding performance for breast cancer detection and classification on different types of datasets compared to the state of the art methods thus verifying its robustness and effectiveness.</div></div>\",\"PeriodicalId\":73399,\"journal\":{\"name\":\"Intelligence-based medicine\",\"volume\":\"12 \",\"pages\":\"Article 100290\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2025-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Intelligence-based medicine\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2666521225000948\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Intelligence-based medicine","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2666521225000948","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A hybrid feature extraction based ensemble model for breast cancer detection and classification using different medical images
Breast cancer is a fatal disease that has a high death rate worldwide, according to the WHO. Hence, implementing medical image-based automated breast cancer detection and classification is essential for early identification and categorization. It plays a crucial role in developing efficient treatment methods by accurately diagnosing the kind and classifying the subtype of breast cancer. Ultrasound and mammograms are primary and efficient methods for detection, whereas histopathology is an advanced method for exactly classifying breast cancer. Previously, different hand-engineered features were used for different types of data sets, respectively, which provided high accuracy individually. However, deep learning is a strong tool for computer vision tasks. Therefore, we developed a unique combination of hand-engineered features for color, shape, and texture extraction in parallel to three different deep neural networks. Such a hybrid method proposed that combines both hand-engineered and deep learning-based feature extractors provides an outstanding performance for breast cancer detection and classification on different types of datasets compared to the state of the art methods thus verifying its robustness and effectiveness.