Classification of Healthy and Cancer States of Colon Epithelial Tissues Using Opto-magnetic Imaging Spectroscopy

Analysis of Colorectal and Gastric Cancer Classification: A Mathematical Insight Utilizing Traditional Machine Learning Classifiers

Cancer remains a formidable global health challenge, claiming millions of lives annually. Timely and accurate cancer diagnosis is imperative. While numerous reviews have explored cancer classification using machine learning and deep learning techniques, scant literature focuses on traditional ML methods. In this manuscript, we undertake a comprehensive review of colorectal and gastric cancer detection specifically employing traditional ML classifiers. This review emphasizes the mathematical underpinnings of cancer detection, encompassing preprocessing techniques, feature extraction, machine learning classifiers, and performance assessment metrics. We provide mathematical formulations for these key components. Our analysis is limited to peer-reviewed articles published between 2017 and 2023, exclusively considering medical imaging datasets. Benchmark and publicly available imaging datasets for colorectal and gastric cancers are presented. This review synthesizes findings from 20 articles on colorectal cancer and 16 on gastric cancer, culminating in a total of 36 research articles. A significant focus is placed on mathematical formulations for commonly used preprocessing techniques, features, ML classifiers, and assessment metrics. Crucially, we introduce our optimized methodology for the detection of both colorectal and gastric cancers. Our performance metrics analysis reveals remarkable results: 100% accuracy in both cancer types, but with the lowest sensitivity recorded at 43.1% for gastric cancer.

Opto-magnetic imaging spectroscopy in analyzing rotary NiTi endodontic instruments

Intra-canal fracture of rotary NiTi instruments occurs due to torsional stress, cyclic fatigue, or a combination of these two factors. Broken instruments are a serious obstacle to shaping, cleaning, and filling of the root canal and can adversely affect the outcome of endodontic therapy. The aim of this study was to examine the magnetic properties and ultrastructural changes of new, used, and fractured NiTi instruments using opto-magnetic imaging spectroscopy (OMIS). The study included three sets of different types of rotary instruments: MTwo (VDW, Munich, Germany), Pro Taper Universal (Dentsply Maillefer, Ballaigues, Switzerland), and BioRace (FKG DENTAIRE Swiss Dental Products, Le Crêt-du-Locle Switzerland). Root canal shaping was performed on root canals with different curvatures, and after intra-canal fracture, instruments of the same type (new, used, and fractured) were analyzed using OMIS at the Faculty of Mechanical Engineering, University of Belgrade. The obtained results showed a coincidence of peak localization for the used instruments that did not suffer a fracture, as well as for new, unused instruments of all examined groups. Additionally, there was a coincidence of peak intensities for new and fractured instruments in all groups. The specific treatment of electropolishing of the active surface of BioRace instruments caused a completely different electromagnetic response compared to conventional NiTi sets of tested instruments. New, unused BioRace instruments had the most pronounced positive (5.6078 n.a.u. x1000) and negative (-8.5218 n.a.u. x1000) intensity values. The analysis of NiTi instruments using opto-magnetic imaging spectroscopy indicated changes in the magnetic properties after their instrumentation.

Colon Cancer Diagnosis Based on Machine Learning and Deep Learning: Modalities and Analysis Techniques

The treatment and diagnosis of colon cancer are considered to be social and economic challenges due to the high mortality rates. Every year, around the world, almost half a million people contract cancer, including colon cancer. Determining the grade of colon cancer mainly depends on analyzing the gland's structure by tissue region, which has led to the existence of various tests for screening that can be utilized to investigate polyp images and colorectal cancer. This article presents a comprehensive survey on the diagnosis of colon cancer. This covers many aspects related to colon cancer, such as its symptoms and grades as well as the available imaging modalities (particularly, histopathology images used for analysis) in addition to common diagnosis systems. Furthermore, the most widely used datasets and performance evaluation metrics are discussed. We provide a comprehensive review of the current studies on colon cancer, classified into deep-learning (DL) and machine-learning (ML) techniques, and we identify their main strengths and limitations. These techniques provide extensive support for identifying the early stages of cancer that lead to early treatment of the disease and produce a lower mortality rate compared with the rate produced after symptoms develop. In addition, these methods can help to prevent colorectal cancer from progressing through the removal of pre-malignant polyps, which can be achieved using screening tests to make the disease easier to diagnose. Finally, the existing challenges and future research directions that open the way for future work in this field are presented.