Open Access

REVIEW

Deep Learning-Based Cancer Detection-Recent Developments, Trend and Challenges

Gulshan Kumar^1,*, Hamed Alqahtani²

1 Shaheed Bhagat Singh State University, Ferozepur, India
2 King Khalid University, Abha, Saudi Arabia

* Corresponding Author: Gulshan Kumar. Email:

Computer Modeling in Engineering & Sciences 2022, 130(3), 1271-1307. https://doi.org/10.32604/cmes.2022.018418

Received 23 July 2021; Accepted 26 September 2021; Issue published 30 December 2021

Abstract

Cancer is one of the most critical diseases that has caused several deaths in today’s world. In most cases, doctors and practitioners are only able to diagnose cancer in its later stages. In the later stages, planning cancer treatment and increasing the patient’s survival rate becomes a very challenging task. Therefore, it becomes the need of the hour to detect cancer in the early stages for appropriate treatment and surgery planning. Analysis and interpretation of medical images such as MRI and CT scans help doctors and practitioners diagnose many diseases, including cancer disease. However, manual interpretation of medical images is costly, time-consuming and biased. Nowadays, deep learning, a subset of artificial intelligence, is gaining increasing attention from practitioners in automatically analysing and interpreting medical images without their intervention. Deep learning methods have reported extraordinary results in different fields due to their ability to automatically extract intrinsic features from images without any dependence on manually extracted features. This study provides a comprehensive review of deep learning methods in cancer detection and diagnosis, mainly focusing on breast cancer, brain cancer, skin cancer, and prostate cancer. This study describes various deep learning models and steps for applying deep learning models in detecting cancer. Recent developments in cancer detection based on deep learning methods have been critically analysed and summarised to identify critical challenges in applying them for detecting cancer accurately in the early stages. Based on the identified challenges, we provide a few promising future research directions for fellow researchers in the field. The outcome of this study provides many clues for developing practical and accurate cancer detection systems for its early diagnosis and treatment planning.

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