Introduction
MATLABSolutions describes the process of designing a drugged cell and normal cell detection program using deep learning with the help of MATLAB. Detection of drugged cell can be used to detect cancer cells. This MATLAB simulation can turn into a great tool to detect the cancer causing cells and can greatly help researchers /doctor to find a cure for cancer. Simulation of the system has been carried out in MATLAB/SIMULINK with different images and it gives various results according to the input given. This video gives idea about program code for designing drugged cell detection program.
Methodology
The development of a drugged cell and normal cell detection program using deep learning in MATLAB, as described by MATLABSolutions, represents a significant advancement in medical imaging and cancer research. This innovative approach leverages deep learning algorithms to analyze and differentiate between drugged cells, which may include cancerous cells, and healthy cells based on their visual characteristics in medical images. By utilizing MATLAB's robust computational capabilities and SIMULINK for simulation, researchers can process a variety of images to train and test the model, ensuring accurate identification of cancerous cells. The system's ability to handle diverse inputs and produce reliable results makes it a powerful tool for medical professionals and researchers aiming to detect cancer-causing cells early, thereby aiding in the development of targeted treatments and potentially improving patient outcomes.The MATLAB-based simulation provides a user-friendly platform for designing and testing the cell detection program, with the accompanying video tutorial offering valuable insights into the coding process. By simulating different scenarios with varied image inputs, the program generates results that reflect the unique properties of each cell type, enabling precise differentiation. This technology has the potential to revolutionize cancer diagnostics by providing a non-invasive, efficient method to identify malignant cells, which could accelerate research into cancer cures. The integration of deep learning with MATLAB/SIMULINK not only enhances the accuracy of detection but also streamlines the workflow for researchers, making it an invaluable asset in the fight against cancer.