Multi-objective deep learning for lung cancer detection in CT images: enhancements in tumor classification, localization, and diagnostic efficiency
The proposed model offers a robust and scalable solution for lung cancer detection, providing real-time inference, multi-task learning, and high accuracy. It holds significant potential for clinical integration to improve diagnostic outcomes and patient care.
Authors
- Bagher Farhood (ORCID: https://orcid.org/0000-0003-2290-7220)
- Anupam Yadav (ORCID: https://orcid.org/0000-0002-9179-3151)
- V. Kavitha (ORCID: https://orcid.org/0000-0002-7287-4714)
- Shaymaa Mohammed Abdulameer
- Mahmood Jasem Jawad (ORCID: https://orcid.org/0000-0001-6542-7230)
- Ashok Kumar Bishoyi (ORCID: https://orcid.org/0000-0001-6810-2511)
- Hiba Mushtaq
- Zafar Aminov (ORCID: https://orcid.org/0000-0002-5910-7465)
- Abdulqader Faris Abdulqader
- M. M. Rekha (ORCID: https://orcid.org/0000-0002-7582-2522)
- Mariem Alwan
- Zahraa Saad Abdulali
- Mayank Kundlas
Institutions
- Jain University (IN)
- University of Babylon (IQ)
- Kashan University of Medical Sciences (IR)
- Ahl Al Bayt University (IQ)
- Marwadi University (IN)
- Al-Nisour University College (IQ)
- Samarkand State Medical Institute (UZ)
- University of Kerbala (IQ)
- Sathyabama Institute of Science and Technology (IN)
- Al-Farahidi University
- Chitkara University (IN)
- GLA University (IN)
Publication Details
- Journal
- Discover Oncology
- Published
- 2025-04-15
- DOI
- https://doi.org/10.1007/s12672-025-02314-8
- Citations
- 9
- Primary Topic
- Lung Cancer Diagnosis and Treatment
- Type
- article
- Field-Weighted Citation Impact
- 13.67