{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,2]],"date-time":"2026-02-02T11:58:22Z","timestamp":1770033502085,"version":"3.49.0"},"reference-count":38,"publisher":"MDPI AG","issue":"22","license":[{"start":{"date-parts":[[2022,11,18]],"date-time":"2022-11-18T00:00:00Z","timestamp":1668729600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Lung cancer is the leading cancer type that causes mortality in both men and women. Computer-aided detection (CAD) and diagnosis systems can play a very important role for helping physicians with cancer treatments. This study proposes a hierarchical deep-fusion learning scheme in a CAD framework for the detection of nodules from computed tomography (CT) scans. In the proposed hierarchical approach, a decision is made at each level individually employing the decisions from the previous level. Further, individual decisions are computed for several perspectives of a volume of interest. This study explores three different approaches to obtain decisions in a hierarchical fashion. The first model utilizes raw images. The second model uses a single type of feature image having salient content. The last model employs multi-type feature images. All models learn the parameters by means of supervised learning. The proposed CAD frameworks are tested using lung CT scans from the LIDC\/IDRI database. The experimental results showed that the proposed multi-perspective hierarchical fusion approach significantly improves the performance of the classification. The proposed hierarchical deep-fusion learning model achieved a sensitivity of 95% with only 0.4 fp\/scan.<\/jats:p>","DOI":"10.3390\/s22228949","type":"journal-article","created":{"date-parts":[[2022,11,21]],"date-time":"2022-11-21T04:39:59Z","timestamp":1669005599000},"page":"8949","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Multi-Perspective Hierarchical Deep-Fusion Learning Framework for Lung Nodule Classification"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3905-0294","authenticated-orcid":false,"given":"Kazim","family":"Sekeroglu","sequence":"first","affiliation":[{"name":"Department of Computer Science, Southeastern Louisiana University, Hammond, LA 70402, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8832-9171","authenticated-orcid":false,"given":"\u00d6mer Muhammet","family":"Soysal","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Southeastern Louisiana University, Hammond, LA 70402, USA"},{"name":"School of Electrical Engineering and Computer Science, Louisiana State University, Baton Rouge, LA 70803, USA"}]}],"member":"1968","published-online":{"date-parts":[[2022,11,18]]},"reference":[{"key":"ref_1","unstructured":"American Cancer Society (2016). 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