Artificial Intelligence Techniques for Cancer Detection and Classification: Review Study

Abstract

Cancer is the general name for a group of more than 100 diseases. Although cancer includes different types of diseases, they all start because abnormal cells grow out of control. Without treatment, cancer can cause serious health problems and even loss of life. Early detection of cancer may reduce mortality and morbidity. This paper presents a review of the detection methods for lung, breast, and brain cancers. These methods used for diagnosis include artificial intelligence techniques, such as support vector machine neural network, artificial neural network, fuzzy logic, and adaptive neuro-fuzzy inference system, with medical imaging like X-ray, ultrasound, magnetic resonance imaging, and computed tomography scan images. Imaging techniques are the most important approach for precise diagnosis of human cancer. We investigated all these techniques to identify a method that can provide superior accuracy and determine the best medical images for use in each type of cancer.

Artificial intelligence as an innovative approach for investment in the future of healthcare in Egypt

“…Regarding segments of AI in healthcare, the results of the present study revealed that the entire study sample was not aware of those segments. There was inconsistency with Al-shamasneh [3] and Erguzel [4] who stated that hospitals, either governmental or private healthcare centers and nursing homes are considered segments for AI use in healthcare. For example, in oncology AI can be used to aid doctors in the diagnosis and treatment of seven types of cancer.…”

“…There is a paradigm shift to digitalize healthcare sectors to get a competitive advantage in the labor market. [3,4] AI has managed to capture the attention of key healthcare stakeholders, who are now in a dilemma of whether or not to integrate it fully or partially in their businesses. [5,6] The name behind the idea of AI is John McCarthy, who began research on the subject in 1955 and assumed that each aspect of learning and other domains of intelligence could be described so precisely that they can be simulated by a machine.…”

“…[9,14,16] With the aging infrastructure in some countries and demand for more beds in others, hospital executives and governments should rethink how best to optimize inpatient and outpatient settings to connect with consumers, integrate digital technologies into traditional hospital services such as telenursing and telemedicine to truly create a health system without walls. [3,4] 1.1 Significance of the study In a time of rapid healthcare transformation, health organizations must quickly adapt to evolving technologies, regulations, and consumer demands. AI offers the industry incredible potential to learn from past experiences and make better decisions for the future.…”

Adoption of machine learning for medical diagnosis

“…Clinical treatment driven by AI has the potential to make a significant difference in eliminating health inequities, especially in low-resource settings. AI integration in cancer care can increase diagnosis accuracy and speed, aid healthcare decision, and result in improved health outcomes (Al-shamasneh & Obaidellah, 2017). Artificial intelligence (AI) has the potential to improve cancer screening, tumor genetic characterization, drug discovery, and cancer surveillance.…”

An application of ANFIS for Lung Diseases Early Detection System

“…The expert system in health is not intended to replace the role of a doctor, but to help diagnose the patient's condition earlier [10]. Artificial Intelligence Techniques for Cancer Detection and Classification have been investigated [11]. Expert system as a tool to diagnose diseases with out of breath symptoms and also provide treatment solutions can be carried out through consultation by answering each a yes or no question [12].…”