Assessment of a Neural Network Based on Texture Features Analysis: The Impact of Classifying Cancer Types Using Image Processing

Traditional histopathology examination remains a serious task in cancer identification and is clinically vital to division the cancer tissues and group them into numerous classes. However, the diagnostic process is subjective, and the variations among technical observers and time consumed are considerable. Reliable, automated cancer detection assistance is currently an increasingly important task in the medical field. This study aims to classify different cancer tumor types. A comprehensive analysis of a new classification technique based on image processing and composition properties was performed. A graphical user interface (GUI) program dedicated to the classification and identification of cancer cell images was developed and created in-house using Matlab package. As a result, the data can improve the diagnostic capabilities of physicians and reduce the time required for precise diagnosis. The average discrimination rate demonstrates the validity of the proposed technique in distinguishing between benign and malignant lesions. This simple procedure is an encouraging application of digital image processing performance in the histopathology field compared with traditional methods. Further investigations in the future may demonstrate a great advantage in the prediction and classification of cell morphology and cancer grading using the computed segmentation technique