Cancer Cell Classification using Deep Learning

TL;DR

This paper explores the use of deep learning algorithms to classify cancer cells as benign or malignant, aiming to improve early detection and treatment outcomes.

Contribution

It introduces a deep learning-based approach for classifying cancer cells, comparing various models to identify the most effective method.

Findings

Deep learning models achieved high accuracy in classifying cancer cells.

The study demonstrates the potential of data mining techniques in medical diagnosis.

Multiple algorithms were evaluated to optimize classification performance.

Abstract

In the current technological era, the medical profession has emerged as one of the researchers' favorite subject areas, and cancer is one of them. Because there is now no effective treatment for this illness, it is a matter of concern. Only if this disease is discovered early may patients be rescued (stage I and stage II). The likelihood of survival is quite low if it is discovered in later stages (stages III and IV). The application of machine learning, deep learning, and data mining techniques in the medical industry has the potential to address current issues and bring benefits. Numerous symptoms of cancer exist, including tumors, unusual bleeding, increased weight loss, etc. It is not necessary for all tumor types to be cancerous. There are two sorts of tumors: benign and malignant. To give patients, the right care, symptoms must be carefully examined, and an automated system is to distinguish between benign and malignant tumors. Most data produced in today's online environment comes from websites related to healthcare or social media. Using data mining techniques, it is possible to extract symptoms from this vast amount of data, which will be helpful for identifying or classifying cancer. This research classifies bacteria cells as benign or cancerous using various deep-learning Algorithms. To get the best and most reliable results for the classification, a variety of methodologies and models are trained and improved.