Deep Learning Impacts on Cancer Diagnosis - Theory, Method, and Applications

Main Article Content

G. M. Sasikala
K. Anand
R. Pugalenthi


Medical imaging permits visualization and quantitative studies of genetic improvement of biological processes that are of high importance for the early detection of cancer. In this research, we deliver a study on the aspects of cancer detection and diagnosis that leverage deep learning. Secondly, we also outline a general deep learning model for cancer detection. Third, we include a review and feedback on the latest work on deep learning systems for the identification and diagnosis of cancer and some future research directions. Deep learning has been widely used in the last couple of years in medical imaging science, as it is constrained by meritocratic abilities of high representative capability in visual evaluation and conventional machine learning methods. Deep learning as a wider model is suggested, requires less computer technology and allows a forecast with more reliable data volumes. This article gives the comparative study on deep learning impacts on Cancer Diagnosis. We describe aspects of deep cancer therapy for the first time in this document, including steps for cancer diagnosis with doctor-style phases. Applications and research guidelines are given in the last part of this manuscript which shows how deep learning models were successful for various types of cancer. A review and feedback on the latest work on deep learning systems for the identification and diagnosis of cancer and some future research directions.

Article Details

How to Cite
Sasikala, G. M. ., K. Anand, & Pugalenthi, R. . (2023). Deep Learning Impacts on Cancer Diagnosis - Theory, Method, and Applications. Journal of Coastal Life Medicine, 11(1), 532–547. Retrieved from


Ali, Abder-Rahman, Deep Learning in Oncology–Applications in Fighting Cancer,2017.

Dudani, Jaideep& Warren, Andrew & Bhatia, Sangeeta,Harnessing Protease Activity to Improve Cancer Care. Annual Review of Cancer Biology,2018. 2. 10.1146/annurev-cancerbio-030617-050549.

Fakoor, Rasool&Ladhak, Faisal & Nazi, Azade& Huber, Manfred,Using deep learning to enhance cancer diagnosis and classification. Proceedings of the ICML Workshop on the Role of Machine Learning in Transforming Healthcare,20134.

Hajela, Priyank&Pawar, Dr. Ambika&Ahirrao, Swati, Deep Learning for Cancer Cell ,etection and Segmentation: A Survey,2018. 1-6. 10.1109/PUNECON .2018.8745420.

Hamed, Ghada&Marey, Mohammed & Amin, Safaa&Tolba, Mohamed,Deep Learning in Breast Cancer Detection and Classification,2020 10.1007/978-3-030-44289-7_30.

Hu, Zilong& Tang, Jinshan& Wang, Ziming& Zhang, Kai & Zhang, Lin & Sun, Qingling, Deep Learning for Image-based Cancer Detection and Diagnosis — A Survey. Pattern Recognition,2018,83. 10.1016/j.patcog.2018.05.014.

Koc, Pinar &Yalcin, Cihan,Future of Deep Learning for Cancer Diagnosis,2020. 10.1007/ 978-981 -15-6321-8_13

Koul, Bhupendra,Types of Cancer,2019, 10.1007/978-981-32-9147-8_2.

Kumar, S N & Fred, A. &Padmanabhan, Parasuraman&Gulyas, Balazs&Haridhas, Ajay Kumar & Miriam, Jonisha,Deep Learning Algorithms in Medical Image Processing for Cancer Diagnosis: Overview, Challenges and Future,2020, 10.1007/978-981-15-6321-8_3.

Levine, Adrian & Schlosser, Colin &Grewal, Jasleen&Coope, Robin & Jones, Steve & Yip, Stephen, Rise of the Machines: Advances in Deep Learning for Cancer Diagnosis. Trends in Cancer,2019,(5). 10.1016/j.trecan .2019.02.002.

Lollini, Pier-Luigi &Cavallo, Federica &Nanni, Patrizia&Quaglino, Elena, The Promise of Preventive Cancer Vaccines,2015,(3). 467-489. 10.3390/vaccines3020467.

Mittal, Mamta&Goyal, Lalit&Kaur, Sumit&Kaur, Iqbaldeep&Verma, Amit& D, Jude,Deep learning based enhanced tumor segmentation approach for MR brain images. Applied Soft Computing,2019,78. 10.1016/j.asoc.2019.02.036.

Munir, Khushboo&Elahi, Hassan &Ayub, Afsheen&Frezza, Fabrizio&Rizzi, Antonello, Cancer Cancer Diagnosis Using Deep Learning: A Bibliographic Review. Cancers. 2019,11. 10.3390/ cancers 11091235.

Rakash, Syed. (2012). Role of proteases in cancer: A review. Biotechnology and Molecular BiologyReviews,2017,(7)90-101,10.5897/BMBR11.027.

Sekaran, Karthik&Ramalingam, SrinivasaPerumal& P.V.S.S.R., Chandra Mouli. Breast Cancer Classification Using Deep Neural Networks,2018.. 10.1007/978-981-10-6680-1_12.

Xue, Yong & Chen, Shihui& Qin, Jing & Liu, Yong & Huang, Bingsheng& Chen, Hanwei. Application of Deep Learning in Automated Analysis of Molecular Images in Cancer: A Survey. Contrast Media & Molecular Imaging, 2017. 1-10. 10.1155/2017/9512370.

Yari, Yasin& Nguyen, Thuy& Nguyen, Hieu, Deep Learning Applied for Histological Diagnosis of Breast Cancer,2020 IEEE Access. 8. 10.1109/ACCESS.2020.3021557.

A.SherylOliver,Kavitha Ganesan,S.A.Yuvaraj,T.Jayasankar,Mohamed Yacin Sikkandar N.B Prakash, Accurate prediction of heart disease based on bio system using regressive learning based neural network classifier”, Journal of Ambient Intelligence and Humanized Computing, 2021,Springer Nature.

Pugalenthi, R., Oliver, A.S., Anuradha, M., Impulse noise reduction using hybrid neuro-fuzzy filter with improved firefly algorithm from X-ray bio-images, International Journal of Imaging Systems and Technology,2020.