A Survey of Computer-Aided Tumor Diagnosis Based on Convolutional Neural Network.

Yan Yan,Shui-Hua Wang,Xu-Jing Yao,Yu-Dong Zhang

doi:10.3390/biology10111084

Abstract

Simple SummaryOne of the hottest areas in deep learning is computerized tumor diagnosis and treatment. The identification of tumor markers, the outline of tumor growth activity, and the staging of various tumor kinds are frequently included. There are several deep learning models based on convolutional neural networks that have high performance and accurate identification, with the potential to improve medical tasks. Breakthroughs and updates in computer algorithms and hardware devices, and intelligent algorithms applied in medical images have a diagnostic accuracy that doctors cannot match in some diseases. This paper reviews the progress of tumor detection from traditional computer-aided methods to convolutional neural networks and demonstrates the potential of the practical application of convolutional neural networks from practical cases to transform the detection model from experiment to clinical application.Tumors are new tissues that are harmful to human health. The malignant tumor is one of the main diseases that seriously affect human health and threaten human life. For cancer treatment, early detection of pathological features is essential to reduce cancer mortality effectively. Traditional diagnostic methods include routine laboratory tests of the patient’s secretions, and serum, immune and genetic tests. At present, the commonly used clinical imaging examinations include X-ray, CT, MRI, SPECT scan, etc. With the emergence of new problems of radiation noise reduction, medical image noise reduction technology is more and more investigated by researchers. At the same time, doctors often need to rely on clinical experience and academic background knowledge in the follow-up diagnosis of lesions. However, it is challenging to promote clinical diagnosis technology. Therefore, due to the medical needs, research on medical imaging technology and computer-aided diagnosis appears. The advantages of a convolutional neural network in tumor diagnosis are increasingly obvious. The research on computer-aided diagnosis based on medical images of tumors has become a sharper focus in the industry. Neural networks have been commonly used to research intelligent methods to assist medical image diagnosis and have made significant progress. This paper introduces the traditional methods of computer-aided diagnosis of tumors. It introduces the segmentation and classification of tumor images as well as the diagnosis methods based on CNN to help doctors determine tumors. It provides a reference for developing a CNN computer-aided system based on tumor detection research in the future.

Highlights

Different from other deep learning methods of medical image processing, which require a huge number of annotated training data sets, the way proposed by the authors utilizes a subsequent frame structure to generate accurate tumor tracking through tiny training data sets, reducing the problem of manual processing to a certain extent
Barzekar and Yu [119] proposed a convolutional neural network (CNN) architecture, C-NET, which consists of a series of multiple networks to classify biomedical histopathological images from public data sets, including Barzekar and osteosarcoma
Compared with the traditional deep learning model, which uses transfer learning to solve the problem, this model contains multiple CNNs, and the first two parts of the architecture contain six networks of feature extractors to classify tumor images according to malignancy and benignancy

Summary

Introduction

According to the pathological morphology [3], growth mode, cellular characteristics of new organisms, and the degree of harm to the body, a tumor can be classified into malignant or benign. Many of the different types of cancer that develop from primary tumors are hard to detect in their early stages, generally are not detected until late, and often miss the best time for treatment. If the malignant tumor grows in a certain site, the patient may only have local swelling or pain in the early stage. Because the clinical manifestations of benign and malignant tumors are different, the early systemic symptoms of tumors are generally mild and limited. Developing better treatment plans can (i) reduce the rate of misdiagnosis effectively, (ii) promote the efficiency of the entire medical system, and (iii) alleviate the suffering of tumor patients.

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Conclusion