COMPREHENSIVE REVIEW: CANCER TYPE DETECTION USING ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING

Abstract: In recent years, the use of Artificial Intelligence in healthcare is increasing, mainly for detecting cancer. Early and correct diagnosis of cancer plays a crucial role for better treatment results and for lowering the number of deaths. This work describes a deep learning system that is designed to identify and categorize different cancers like brain tumor, skin cancer, lung cancer, and breast cancer, using medical imaging data.

The system uses Convolutional Neural Networks (CNNs), which helps in analyzing images and automatically extract important information from images. Different datasets are collected and organized into groups such as benign, malignant, and normal. Before training, images undergo preparation steps such as resizing, normalization, and data augmentation to improve results and reduce overfitting.

Different CNN models are trained for each cancer type with TensorFlow and Keras frameworks. The performance of these models is measured using metrics such as accuracy and loss. Experimental results show that some models achieved high accuracy (approximately 85–90%), while others demonstrated moderate performance due to challenges such as limited dataset size and class imbalance.

To enhance usability, a simple and interactive graphical user interface (GUI) is developed, allowing users to upload medical images and obtain real-time predictions along with confidence scores. Additionally, an invalid image detection mechanism is incorporated to prevent incorrect predictions for unrelated inputs, thereby improving system reliability.

Overall, this paper demonstrates the effectiveness of deep learning in cancer detection while also highlighting key challenges such as data limitations and model generalization. The proposed system can serve as a foundational framework for future research and can be further improved using advanced architectures, larger datasets, and real-time deployment strategies for practical healthcare applications.

Keywords: Cancer Detection, Medical Imaging, Artificial Intelligence, Machine Learning, Deep Learning, Convolutional Neural Networks (CNN), Healthcare Analytics, Tumor Classification, Clinical Decision Support Systems.