Computational Pathology is a novel field comprising aspects of machine learning, computer vision, clinical statistics and general pathology. The principal focus for this study is to define this new field, develop and investigate statistical methods, which can be combined within a unified framework to answer scientific and clinical questions in pathology. Researchers seek to study and investigate various cancer tumors such as colon, prostatic, testicles, dermal, and uterine cancer, which concern with cancer, asthma, diabetes and cardiovascular diseases. It is known that a lack of oxygen in high places, such as Albaha area, is one of the encouraging factors to activate genes in the operations division of proteins that play a key role in the biology of cancer cells. This study aims at investigating and evaluating different cancer tumor types such as, colon, prostatic, testicles, dermal, and uterine. The aim of this project is to achieve its goal by performing a recent literature search on the classification of tissue and cell morphology using image processing and neural network. Therefore, collecting and preparing histopathological specimens from malignant tissues. Hence, building easy to use software for the classification of cell morphology. This program will be a graphical user interface (GUI) using MATLAB package. The average discrimination rate will be calculated to show the validity of the proposed technique and software in the prediction and classification of cell morphology. Finally, running the proposed program for different cases and discussing the results of prediction and classification.
The proposed system has been tested on colon, prostatic, testicles, dermal, and uterine specimens images (n = 10 for each), and the overall average discrimination rate is about 91.20%. As a result, the data can enhance the diagnostic capabilities of physicians and reduce time required for accurate diagnosis. The average discriminations rate will calculated to perform the validity of the proposed technique in distinguishing benign versus malignant lesions. These modified and simple procedures can find promising application of digital image processing technique to the field of histopathology compared with traditional methods. Further investigations in the futures may demonstrate the great advantage in prediction, classification of cell morphology and cancer grading depending on computed segmentation technique.
This methodology is successfully employed for some diagnostic tests, which may be in the future identify an effective treatment approach from the start, before treatment even begins. Experienced physicians with expertise in diagnosing diseases. Histopathologists can more precisely determine the exact cell type and grade of a tumor.