Machine Learning for Biomedical Application

Strzelecki, Michał; Badura, Paweł

doi:10.3390/app12042022

Cited by 23 publications

(21 citation statements)

References 11 publications

(12 reference statements)

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“…However, traditional classification may not have performed as well as planned, raising the necessity of such investigations that could improve the current classification technologies in medical sectors. The goal of medical AI and ML research is to create applications that use AI technologies to aid practitioners in providing treatment based on better decision making [9][10]. Some research has been conducted on comprehensible AI in order to solve the downfalls of AI analysis tools being black boxes.…”

Section: Literature Reviewsmentioning

confidence: 99%

An Improved Breast Cancer Classification Method Using an Enhanced AdaBoost Classifier

Qawqzeh¹,

Alourani²,

Ghwanmeh³

2023

IJACSA

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The goal of this research is to create a machine learning (ML) classifier that can improve breast cancer (BC) diagnosis and prediction. The principle components analysis (PCA) technique is used in this work to minimize the dimensions of the BC dataset and achieve better classification metrics. The developed classifier outperformed others in terms of F1 score and accuracy score. Using the original BC dataset, four different classifiers are applied to determine the best classifier in terms of performance metrics. The used classifiers were RandomForest, DecisionTree, AdaBoost, and GradientBoosting. The RandomForest classifier obtained (95.7%) f1 score and (94.5%) accuracy score, the DecisionTree classifier obtained (93%) f1 score and (91%) accuracy score, the GradientBoosting classifier obtained (95%) f1 score and (93.5%) accuracy score, and the AdaBoost classifier obtained (95.8%) f1 score and (94.5%). The AdaBoost classifier was utilized to create the final model using the reduced PCA dataset because it scored the highest performance metrics. The developed classifier is named as "pcaAdaBoost". The optimized pcaAdaBoost achieved higher performance metrics in terms of f1 score (99%) and accuracy score (98.8%). The results reveal that the optimized pcaAdaBoost scored highest performance measures in terms of crossvalidation and testing outcomes, with an overall accuracy of (99%). The improved results justify the use of dimensionality reduction in high-dimension datasets to reduce complexity and improve performance measures.

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Section: Literature Reviewsmentioning

confidence: 99%

An Improved Breast Cancer Classification Method Using an Enhanced AdaBoost Classifier

Qawqzeh¹,

Alourani²,

Ghwanmeh³

2023

IJACSA

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“…Several research articles related to this exist, like [8], which focus on biomedical signals techniques for the detection of Covid-19, [9], which centered on monitoring systems remotely for personal health during Covid-19 pandemic using biomedical signals, an overview of the diagnosis of Schizophrenia using a medical image by [10], recent advances, challenges, and way forward of medical image analysis utilizing machine learning techniques by [11], generative Adversarial Networks for Biomedical Image Analysis by [12,13] biomedical applications using machine learning techniques. All these articles focus on medical image processing or biomedical signals.…”

Section: Introductionmentioning

confidence: 99%

Computational Intelligence Approaches for Enhancing Biomedical Image Processing Applications Based on Breast Cancer

Isa¹,

Iliyas²,

Zarma³

2024

Biomedical Engineering

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Recent advances in the cutting-edge technologies of biomedical sensing and image processing tools provide us with big data of biomedical and various types of images that can’t be processed within a finite period by professional clinicians. Various techniques for processing biomedical images comprise mathematical algorithms that extract vital diagnostic features from biomedical information and biological data. Because of the complexity and big size of the data computation, intelligence techniques have been applied in processing, visualizing, diagnostic, and classification tasks. This study will explore the effectiveness of the variously artificial intelligence approaches on biomedical signal and image processing applications. The researchers and community entirely will benefit from this study as a guide to the state-of-the-art artificial intelligence techniques for biomedical signal and image processing applications.

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“…To understand the behavior and pattern of things around the world, algorithms for ITs were designed to let them learn better in fulfilling their jobs and thus save more human efforts and time when dealing with things. They are considered as a way of refinement to make human life even easier by improving machines, which has been widely used in many tasks [1][2][3]. One main way for algorithms to put into use in the network is for identifying various kinds of objects, mostly for helping to maintain social security by performing face or fingerprint recognition process, or by locating a certain object in a vast area.…”

Section: Introductionmentioning

confidence: 99%

Identification for Red Automobile Based on Cifar-10 Using Machine Learning Models

2023

J. Phys.: Conf. Ser.

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Algorithms were made and improved across time in different application fields to help them better fit and solve problems that human encounter. Specifically, this paper focuses on the identification mission, that is commonly needed and used by governmental facilities and police departments to track certain objects. However, it is not always that easy, and methods were made to help invent and test these algorithms, along with a vast dataset collected for use, so that they can be examined before putting into real-life use. An existing dataset called Cifar-10 was introduced and chosen, and different methods were introduced and used, to design and examine the accuracy of an identification method. This paper mainly focuses on a red automobile identification model. The experimental results demonstrated the effectiveness of the model. Further usages of similar models will also be applicable with corresponding adjustments, hoping to make it into other similar areas and fulfilling similar goals.

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Machine Learning for Biomedical Application

Abstract: The tremendous development of technology also affects medical science, including imaging diagnostics [...]

Cited by 23 publications

References 11 publications

An Improved Breast Cancer Classification Method Using an Enhanced AdaBoost Classifier

An Improved Breast Cancer Classification Method Using an Enhanced AdaBoost Classifier

Computational Intelligence Approaches for Enhancing Biomedical Image Processing Applications Based on Breast Cancer

Identification for Red Automobile Based on Cifar-10 Using Machine Learning Models

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