Medical Image Classification Based on Machine Learning Techniques

2019 ◽  
pp. 91-101
Author(s):  
Naziya Pathan ◽  
Mukti E. Jadhav
Keyword(s):  
Machine Learning ◽  
Image Classification ◽  
Medical Image ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
Medical Image Classification

An Overview of Machine Learning in Medical Image Analysis

2017 ◽  
pp. 36-58 ◽  
Author(s):  
Anand Narasimhamurthy
Keyword(s):  
Machine Learning ◽  
Image Analysis ◽  
Medical Imaging ◽  
Health Informatics ◽  
Medical Image ◽  
Medical Image Analysis ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
The Common ◽  
Applications Of Machine Learning

Medical image analysis is an area which has witnessed an increased use of machine learning in recent times. In this chapter, the authors attempt to provide an overview of applications of machine learning techniques to medical imaging problems, focusing on some of the recent work. The target audience comprises of practitioners, engineers, students and researchers working on medical image analysis, no prior knowledge of machine learning is assumed. Although the stress is mostly on medical imaging problems, applications of machine learning to other proximal areas will also be elucidated briefly. Health informatics is a relatively new area which deals with mining large amounts of data to gain useful insights. Some of the common challenges in health informatics will be briefly touched upon and some of the efforts in related directions will be outlined.

scholarly journals Genetic programming for region detection, feature extraction, feature construction and classification in image data

2020 ◽  
Author(s):  
Andrew Lensen ◽  
Harith Al-Sahaf ◽  
Mengjie Zhang ◽  
Bing Xue
Keyword(s):  
Machine Learning ◽  
Image Analysis ◽  
Genetic Programming ◽  
Image Classification ◽  
Search Space ◽  
Machine Learning Techniques ◽  
Efficient Manner ◽  
Speeded Up Robust Features ◽  
Learning Techniques ◽  
High Level

© Springer International Publishing Switzerland 2016. Image analysis is a key area in the computer vision domain that has many applications. Genetic Programming (GP) has been successfully applied to this area extensively, with promising results. Highlevel features extracted from methods such as Speeded Up Robust Features (SURF) and Histogram of Oriented Gradients (HoG) are commonly used for object detection with machine learning techniques. However, GP techniques are not often used with these methods, despite being applied extensively to image analysis problems. Combining the training process of GP with the powerful features extracted by SURF or HoG has the potential to improve the performance by generating high-level, domain-tailored features. This paper proposes a new GP method that automatically detects different regions of an image, extracts HoG features from those regions, and simultaneously evolves a classifier for image classification. By extending an existing GP region selection approach to incorporate the HoG algorithm, we present a novel way of using high-level features with GP for image classification. The ability of GP to explore a large search space in an efficient manner allows all stages of the new method to be optimised simultaneously, unlike in existing approaches. The new approach is applied across a range of datasets, with promising results when compared to a variety of well-known machine learning techniques. Some high-performing GP individuals are analysed to give insight into how GP can effectively be used with high-level features for image classification.

scholarly journals Use case of no code machine learning tools for medical image classification.

2021 ◽  
Author(s):  
Ashwini Kalshetty ◽  
Sutapa Rakshit
Keyword(s):  
Machine Learning ◽  
Medical Imaging ◽  
Image Classification ◽  
Medical Image ◽  
Whole Body ◽  
Learning Tools ◽  
Early Adoption ◽  
Iodine 131 ◽  
Medical Image Classification ◽  
Basic Medical

Abstract AI tools are making paradigm chanegs in the field of medical imaging. Currently, the development of AI tools and their validation for clinical use is heterogenously distributed with the end-users (i.e the physians or radiologists) adopting the software solution. As we are all progressing towards democratization of AI, no code tools offer a versatile and convenient; but largely under-utilized method for medical imaging tasks. Purpose: As a proof-of-concept study, we attempted to evaluate whether no-code machine learning (ML) tools like teachable machine could perform a basic medical image classification task. Methods: We selected 85 cases from our imaging database whose planar whole body Iodine-131 diagnostic scans were labelled into 2 classes as “No evidence of disease” (NED) and “abnormal for training and testing the model. Results: The model generated could accurately classify all NED cases (100%) and abnormal cases with 93% accuracy. Conclusion: We propose that no-code ML tools can perform simple medical image tasks easily. Validation on multiple source larger datasets may allow early adoption of this technology by imaging specialists.

scholarly journals Comparative study of Image Classification Algorithms

2021 ◽  
Vol 7 (01) ◽  
pp. 88-92
Author(s):  
Utkarsh Pandey, Himanshu Aneja Deepanshu Jindal and Ajay Tiwari
Keyword(s):  
Machine Learning ◽  
Image Classification ◽  
Nearest Neighbor ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Binary Decision Tree ◽  
Learning Techniques ◽  
Vector Machines ◽  
Execution Models

This investigation analyzed five common machine learning techniques for performing image classification included Support Vector Machines (SVM), K-Nearest Neighbor (KNN), Naïve Bayes (NB), Binary Decision Tree (BDT) and Discriminant Analysis (DA). AlexNet deep learning model was utilized to fabricate these machine learning classifiers. The structure classifiers were executed and assessed by standard execution models of Accuracy (ACC), Precision (P), Sensitivity (S), Specificity (Spe) and Area Under the ROC Curve (AUC). The five strategies were assessed utilizing 2608 histopathological pictures for head and neck cancer. The examination was directed utilizing multiple times 10-overlay cross validation. For every strategy, the pre-trained AlexNet network was utilized to separate highlights from the activation layer. The outcomes outlined that, there was no contrast between the consequences of SVM and KNN. Both have the equivalent and the higher accuracy than others were 99.98 %, though 99.81%, 97.32% and 93.68% for DA, BDT and NB, separately. The current examination shows that the SVM, KNN and DA are the best techniques for classifying our dataset images.

Comparison of medical image classification accuracy among three machine learning methods

2018 ◽  
Vol 26 (6) ◽  
pp. 885-893 ◽  
Author(s):  
Tomoko Maruyama ◽  
Norio Hayashi ◽  
Yusuke Sato ◽  
Shingo Hyuga ◽  
Yuta Wakayama ◽  
...  
Keyword(s):  
Machine Learning ◽  
Image Classification ◽  
Classification Accuracy ◽  
Medical Image ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Medical Image Classification
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