<HashMap><database>biostudies-literature</database><scores/><additional><omics_type>Unknown</omics_type><volume>13</volume><submitter>Bhattacharjee A</submitter><pubmed_abstract>Lung cancer is a fatal disease caused by an abnormal proliferation of cells in the lungs. Similarly, chronic kidney disorders affect people worldwide and can lead to renal failure and impaired kidney function. Cyst development, kidney stones, and tumors are frequent diseases impairing kidney function. Since these conditions are generally asymptomatic, early, and accurate identification of lung cancer and renal conditions is necessary to prevent serious complications. Artificial Intelligence plays a vital role in the early detection of lethal diseases. In this paper, we proposed a modified Xception deep neural network-based computer-aided diagnosis model, consisting of transfer learning based image net weights of Xception model and a fine-tuned network for automatic lung and kidney computed</pubmed_abstract><journal>Frontiers in oncology</journal><pagination>1193746</pagination><full_dataset_link>https://www.ebi.ac.uk/biostudies/studies/S-EPMC10272771</full_dataset_link><repository>biostudies-literature</repository><pubmed_title>A multi-class deep learning model for early lung cancer and chronic kidney disease detection using computed tomography images.</pubmed_title><pmcid>PMC10272771</pmcid><pubmed_authors>Bhattacharjee A</pubmed_authors><pubmed_authors>Shazly GA</pubmed_authors><pubmed_authors>Selim HMRM</pubmed_authors><pubmed_authors>Sahu RK</pubmed_authors><pubmed_authors>Salem Bekhit MM</pubmed_authors><pubmed_authors>Rabea S</pubmed_authors><pubmed_authors>Elkaeed EB</pubmed_authors><pubmed_authors>Murugan R</pubmed_authors></additional><is_claimable>false</is_claimable><name>A multi-class deep learning model for early lung cancer and chronic kidney disease detection using computed tomography images.</name><description>Lung cancer is a fatal disease caused by an abnormal proliferation of cells in the lungs. Similarly, chronic kidney disorders affect people worldwide and can lead to renal failure and impaired kidney function. Cyst development, kidney stones, and tumors are frequent diseases impairing kidney function. Since these conditions are generally asymptomatic, early, and accurate identification of lung cancer and renal conditions is necessary to prevent serious complications. Artificial Intelligence plays a vital role in the early detection of lethal diseases. In this paper, we proposed a modified Xception deep neural network-based computer-aided diagnosis model, consisting of transfer learning based image net weights of Xception model and a fine-tuned network for automatic lung and kidney computed</description><dates><release>2023-01-01T00:00:00Z</release><publication>2023</publication><modification>2025-04-04T09:51:42.229Z</modification><creation>2025-02-19T04:02:08.689Z</creation></dates><accession>S-EPMC10272771</accession><cross_references><pubmed>37333825</pubmed><doi>10.3389/fonc.2023.1193746</doi></cross_references></HashMap>