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.
Highlights
Cancer is a disease that affects all kinds of life of patients, including business life, family life and social life
Paul et al [62] use a pre-trained convolutional neural networks (CNNs) model to detect lung cancer based on computed tomography (CT) scan images
Han et al [63] suggested a hybrid approach of CNN and Deep Belief Networks (DBN) using an end-to-end learning strategy for protecting lung cancer from the images
Summary
Cancer is a disease that affects all kinds of life of patients, including business life, family life and social life. Diagnosis and treatment of cancer is a long and challenging task in. CMES, 2022, vol.130, no.3 comparison to other diseases [1]. Cancer is caused due to the formation of bad neoplasms along with normal division and reproduction of the cells in various organs and tissues [2]. Cancer has been placed at the second position in diseases resulting in death. In 2018, approximately 9.6 million people suffered and died from cancer [3]. 1 and 2 present the top five most dangerous cancer spread in the US (men and women) and the number of deaths in the year 2019 [4]
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