Intelligent Learning Models for Cancer Diagnosis and Treatment: A Comprehensive Analysis
Shahadat Hussain, Shahnawaz Ahmad, Mohammed Wasid, Sneh, Saanvi Chawla, Aakash Sangwan · 2024
Cancer poses a significant global health and economic challenge, with $\mathbf{2 0}$ million new cases and 9.7 million deaths reported in 2022 itself. Early detection of cancer is crucial for improved survival rates, reduced mortality, making it imperative to develop advanced diagnostic tools. Intelligent Learning Models (ILMs) utilizing Deep Learning (DL) and Machine Learning (ML) by leveraging large datasets and complex pattern recognition are posing as the potential diagnostics solution for cancers. This paper reviews the current state of ILMs, evaluates existing models and case study for diagnosis prognosis and treatment of cancers.