Comparative Analysis of Machine Learning Algorithms in Detection of Brain Tumor

Hassan, Shahriar; Hassan, Ali Ahnaf; Marshad, Inan; Hosain, Muhammad Alif Al; Amin, Musarrat; Faisal, Fahim; Nishat, Mirza Muntasir

doi:10.1109/ibdap55587.2022.9907433

Cited by 19 publications

(6 citation statements)

References 45 publications

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“…The study investigated and compared various machine learning algorithms for breast cancer detection and prediction (Hassan et al, 2023), with a particular focus on the LASSO operator. Their findings provide insight into the relative effectiveness of various techniques for identifying breast cancer cases, paving the way for improved diagnostic methods.…”

Section: Literature Reviewmentioning

confidence: 99%

Development of Machine Learning Model for Breast Cancer Prediction from Ultrasound Images

Hindarto,

Hendrata

2024

SinkrOn

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In the past decade, the revolution in information and computing technology has transformed approaches to breast cancer detection and treatment, with Machine Learning technologies offering significant potential in health data analysis. However, the development of accurate and reliable predictive models is faced with the challenges of data heterogeneity and complexity. This research proposes the development and validation of Machine Learning-based classification models using Support Vector Machine and Principal Component Analysis to address these issues, targeting improved accuracy in the early detection of breast cancer. The methodology applied involved the use of a breast cancer dataset from Kaggle, with data analysis conducted through inductive methods to identify relevant patterns. The combination of Support Vector Machine and Principal component Analysis achieved 89% accuracy in medical image classification, proving its efficacy in breast cancer diagnostics and providing a more reliable model for early detection. The implications of these findings are significant, both theoretically and practically, for the fields of Machine Learning and Breast Cancer, expanding the understanding of the applications of advanced data processing techniques. Although this study faces limitations in the variability of the dataset's patient characteristics, the results offer a basis for further development in diagnostic technology while recommending the integration of Deep Learning and Big Data analysis as a direction for future research.

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Section: Literature Reviewmentioning

confidence: 99%

Development of Machine Learning Model for Breast Cancer Prediction from Ultrasound Images

Hindarto,

Hendrata

2024

SinkrOn

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“…Reinforcement learning is a trial-and-error-based learning strategy and is regarded as the best attempt at modeling human-like learning experience [83]. Support vector machine (SVM), linear regression, logistic regression, naïve Bayes classifier (NB), ANN, k-nearest neighbor (kNN), random forest, and decision trees are ML algorithms that are used to understand the relationship among the features and outcome/target [84]. While linear regression (univariate/multivariate) uses a linear line to describe the relationship, logistic regression predicts a sigmoidal relationship between features and the probability of an outcome.…”

Section: Machine Learningmentioning

confidence: 99%

Machine Learning Mediated Advanced Phage and Antimicrobial Therapy- A Futuristic Approach

Shanmugha Mary,

Manik Patil,

Kundu

et al. 2023

Highlights in BioScience

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The emergence of antimicrobial resistance (AMR) has overwhelmed the contemporary curatives and have turned into one of the major challenges in the biomedical sector. With increasing deaths being associated with AMR every year; early detection of pathogens and development of novel drugs and alternative therapies, have all become ad hoc in diagnosis, prognosis and patient survival. Bacteriophage therapy remains a viable strategy to counteract AMR, yet unduly restrained by phage resistance. Phage infection is a natural phenomenon and can be widely manipulated in vitro using advanced techniques including the CRISPR/Cas systems which renders phage therapy an upper hand in comparison to conventional drugs. Phage identification, host range detection, determination of phage-receptor binding efficiency, adsorption rate, phage genome analysis are crucial stages in phage selection and phage cocktail preparation and moreover pivotal in flourishing phage therapy. The ascent of translational research and omics has allowed the development of quick, reliable and precise strategies for phage-based diagnosis and treatment techniques. However, in vitro evaluation of AMR and phage factors as well as storing, processing and analyzing large laboratory data outputs are expensive, time-consuming and labor-intensive. Machine learning (ML) is a utilitarian strategy to organize, store, analyze data sets and more importantly allows prediction of certain features by recognizing patterns in the data sets. With the huge number of research been carried out around the globe and enormous data sets being published and stored in databases, ML can utilize the available data to perform and guide in developing alternative therapeutics. Several ML based tools have been developed to predict resistance in host, phage grouping for cocktail preparation, resistance and lysogenic genes detection, phage genomic evaluation and to understand phage-host interactions. ML also allows the in silico analysis of large samples (drug/phage) and reduces sample size for in vitro evaluation thereby reducing overall costs, time and labor. The present review summarizes the available ML algorithms and corresponding databases used in AMR and phage research. It also emphasizes the status quo of antimicrobial and phage resistance in the healthcare sector and analyses the role of ML in analyzing biological databases in order to predict possible phage/drug-host interaction patterns, phage susceptibility, suitability of phage strains for therapy and recommends the most efficient drug combinations and treatment strategies.

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“…Therefore, numerous researchers have developed various artificial intelligence models to support professional medical practitioners and aid less experienced doctors in diagnosing brain tumors accurately and efficiently (Coupet et al, 2022). Initially, researchers tried to utilize machine learning (ML) algorithms to diagnose brain tumors from MRI scans (Hassan et al, 2022;Rinesh et al, 2022). However, ML techniques have two major shortcomings: first, they require manual features extraction, which is a laborious task, and second, they are unable to take advantage of modern graphics processing units (GPUs), as they are not implemented in that manner.…”

Section: Introductionmentioning

confidence: 99%

CT-γ-Net: A Hybrid Model based on Convolutional Encoder-Decoder and Transformer Encoder for Brain Tumor Localization

Bedi,

Ningshen,

Rani

et al. 2024

JDSIS

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Brain Tumor is a life-threatening disease, and its early diagnosis can save human life. Computer-Aided Brain Tumor Segmentation or Localization in Magnetic Resonance Imaging (MRI) images have emerged as pivotal approaches for expediting the disease diagnosis process. In the past few decades, various researchers combined the strengths of Convolutional Networks and Transformer to perform Brain Tumor Segmentation. However, these models require a large number of trainable weights parameters, and there is still scope for performance improvement in them. To bridge these research gaps, this paper proposes a novel hybrid model named “CT-γ-Net” for effective and efficient Brain Tumor Localization. The proposed CT-γ-Net model follows an Encoder-Decoder structure in which the Convolutional Encoder (CE) and Transformer Encoder (TE) are used for encoding, whereas the Convolutional Decoder (CD) is utilized for decoding the combined output of CE and TE to generate the segmentation masks. In CE and CD components of CT-γ-Net model conventional convolutional layers are replaced by Depth-Wise Separable convolutional layers, as these layers significantly reduce trainable weights parameters. The proposed model achieves 95.5% MeanIoU, 94.82% Dice Score, and 99.24% Pixel Accuracy on publicly available dataset named The Cancer Imaging Archive (TCIA). These experimental results demonstrate that the CT-γ-Net model outperformed other state-of-the-art research works, despite using roughly 28% fewer trainable weights parameters. Hence, the proposed model’s lightweight nature and its high performance, makes it a suitable candidate for deployment on mobile devices, facilitating the precise localization of brain tumor regions in MRI images.

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Comparative Analysis of Machine Learning Algorithms in Detection of Brain Tumor

Cited by 19 publications

References 45 publications

Development of Machine Learning Model for Breast Cancer Prediction from Ultrasound Images

Development of Machine Learning Model for Breast Cancer Prediction from Ultrasound Images

Machine Learning Mediated Advanced Phage and Antimicrobial Therapy- A Futuristic Approach

CT-γ-Net: A Hybrid Model based on Convolutional Encoder-Decoder and Transformer Encoder for Brain Tumor Localization

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