Breast cancer histological images nuclei segmentation and optimized classification with deep learning

Khan, Fawad Salam; Abbasi, Muhammad Inam; Khurram, Muhammad; Mohd, Mohd Norzali Haji; Khan, M. Danial

doi:10.11591/ijece.v12i4.pp4099-4110

Cited by 3 publications

(2 citation statements)

References 20 publications

(21 reference statements)

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“…Recent studies have demonstrated the effective application of convolution neural networks (CNNs) in segmenting medical images across various fields such as breast cancer [9], [10] and lung segmentation [11]. Precise segmentation of the LV region, particularly areas containing scar tissue, provides a robust foundation for precise subsequent segmentation of myocardial scar tissue.…”

Section: Introductionmentioning

confidence: 99%

Automated DeepLabV3+ based model for left ventricle segmentation on short-axis late gadolinium enhancement-magnetic cardiac resonance imaging images

Awang Damit,

Sulaiman,

Osman

et al. 2024

IJECE

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Accurate segmentation of myocardial scar tissue on late gadolinium enhancement-magnetic cardiac resonance imaging (LGE-CMR) is exceptionally vital for clinical applications, enabling precise diagnosis and effective treatment of various cardiac diseases, such as myocardial infarction and cardiomyopathies. However, the ventricle (LV) variations in the size and shape, artifacts, and image resolution of LGE-CMR has made automatic segmentation of myocardial scar tissue more challenging. While many existing approaches delineate the LV myocardium region using multi-modal segmentation, these models may be computationally complex and suffer from misalignment. Therefore, this study proposed an automatic dual-stage DeepLabV3+ based approach tailored for myocardial scar segmentation on short-axis LGE-MRI exclusively. To segment myocardial scar tissue, the second stage employs the segmented LV chamber from the previous stage. The encoder part of the framework utilizes a MobileNetV2 and ResNet50 backbone for the first and second segmentation, respectively, aiming for optimal resolution of feature maps. Both stages tailor an improved Atrous Spatial Pyramid Pooling module in the DeepLabV3+ model with fine-tuned dilated atrous rates to effectively extract the LV chamber and myocardial scar from the complex LGE-MRI background. Based on the results, the proposed dual-stage network recorded an outstanding segmentation performance, with mean Dice score of 96.02% for LV chamber segmentation and 68.01% for scar tissue extraction.

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Section: Introductionmentioning

confidence: 99%

Automated DeepLabV3+ based model for left ventricle segmentation on short-axis late gadolinium enhancement-magnetic cardiac resonance imaging images

Awang Damit,

Sulaiman,

Osman

et al. 2024

IJECE

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“…Breast cancer diagnosis and prediction has garnered a lot of attention in recent years, and numerous ways have been taken to address this issue [9]- [12]. The present focus is on machine learning and the semantic web.…”

Section: Introductionmentioning

confidence: 99%

An Ontological Model based on Machine Learning for Predicting Breast Cancer

Massari¹,

Gherabi²,

Mhammedi³

et al. 2022

IJACSA

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Breast cancer is mostly a female disease, but it may affect men as well even at a considerably lower percentage. An automated diagnosis system should be built for early detection because manual breast cancer diagnosis takes a long time. Doctors have lately achieved significant advances in the early identification and treatment of breast cancer in order to decrease the rate of mortality caused by the latter. Researchers, on the other hand, are analysing large amounts of complicated medical data by employing a combination of statistical and machine learning methodologies to assist clinicians in predicting breast cancer. Various machine learning approaches, including ontology-based Machine Learning methods, have lately played an essential role in medical science by building an automated system that can identify breast cancer. This study examines and evaluates the most popular machine learning algorithms, besides the ontological model based on Machine Learning. Among the classification methods investigated were Naive Bayes, Decision Tree, Logistic Regression, Support Vector Machine, Artificial Neural Network, Random Forest, and k-Nearest Neighbours. The dataset utilized has 683 instances and is available for download from the Kaggle website. The findings are assessed using performance measures generated from the confusion matrix, such as F-Measure, Accuracy, Precision, and Recall. The ontology model surpassed all machine learning techniques, according to the results.

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Semantic segmentation of breast cancer histopathology images using deep learning

Benmabrouk

Gasmi

Bendjenna

et al. 2022

2022 4th International Conference on Pattern Analysis and Intelligent Systems (PAIS)

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Breast cancer histological images nuclei segmentation and optimized classification with deep learning

Cited by 3 publications

References 20 publications

Automated DeepLabV3+ based model for left ventricle segmentation on short-axis late gadolinium enhancement-magnetic cardiac resonance imaging images

Automated DeepLabV3+ based model for left ventricle segmentation on short-axis late gadolinium enhancement-magnetic cardiac resonance imaging images

An Ontological Model based on Machine Learning for Predicting Breast Cancer

Semantic segmentation of breast cancer histopathology images using deep learning

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