An extensible framework for recurrent breast cancer prognosis using deep learning techniques

Reddy, Shiva Shankar; Swaroop, Chigurupati Ravi; Voosala, Priyadarshini; Pilli, Neelima

doi:10.11591/ijeecs.v29.i2.pp931-941

Cited by 4 publications

(3 citation statements)

References 19 publications

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“…This might entail experimenting with new techniques for interpreting and visualising the findings, incorporating new data sources, or using fresh machine learning algorithms. − Evaluate the performance of new models: it is essential to evaluate the performance of the newly developed machine learning models for predicting lung cancer risk by comparing them to existing models [39], [40]. This comparison should consider various critical factors such as precision, interpretability, algorithmic effectiveness, and usability [41].…”

Section: Bulletin Of Electr Eng and Infmentioning

confidence: 99%

Predicting lung cancer risk using explainable artificial intelligence

Shoukat Makubhai,

Pathak,

Chandre

2024

Bulletin EEI

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Lung cancer is a lethal disease that claims numerous lives annually, and early detection is essential for improving survival rates. Machine learning has shown promise in predicting lung cancer risk, but the lack of transparency and interpretability in black-box models impedes the understanding of factors that contribute to risk. Explainable artificial intelligence (XAI) can overcome this limitation by providing a clear and understandable approach to machine learning. In this study, we will use a large patient record dataset to train an XAI-based model that considers various patient information, including lifestyle factors, clinical data, and medical history, for predicting lung cancer risk. We will use different XAI techniques, including decision trees, partial dependence plots, and feature importance, to interpret the model’s predictions. These methods will provide healthcare professionals with a transparent and interpretable framework for screening and treatment decisions concerning lung cancer risk.

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Section: Bulletin Of Electr Eng and Infmentioning

confidence: 99%

Predicting lung cancer risk using explainable artificial intelligence

Shoukat Makubhai,

Pathak,

Chandre

2024

Bulletin EEI

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“…Lung cancer detection methods using computer-aided detection (CAD) have been developed in recent years [8]- [16]. Early identification of lung cancer has been shown to both lower mortality rates and increase the likelihood of survival.…”

Section: Introductionmentioning

confidence: 99%

A review of convolutional neural network-based computer-aided lung nodule detection system

Sari

Sutikno²,

Soesanti³

et al. 2023

IJ-AI

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Worldwide, lung cancer is the leading cause of mortality and rapidly spreads. Lung tissue that is benign does not grow significantly, but lung tissue that is malignant grows rapidly and attacks the body, posing a grave threat to one's health. This paper provides a literature review of computer-aided detection (CAD) systems for lung cancer diagnosis. Preprocessing, segmentation, detection, and classification are the stages of the CAD system. This review divides the preprocessing into three stages: image smoothing, edge sharpening, and noise removal. Additionally, lung segmentation is divided into three stages: histogram-based thresholding, linked component analysis, and lung extraction. The detecting phase aids in decreasing the workload. Several techniques are briefly described, including random forest, support vector machine (SVM), naive bayes, K-nearest neighbor (KNN), and convolutional neural network (CNN). Classification is the final stage; the image is then identified as containing or not possessing nodules. The prospect of incorporating CNN-based deep learning techniques into the CAD system is discussed. This paper is superior to other review studies on this topic due to its comprehensive examination of pertinent literature and structured presentation. We anticipate that our study may aid professional researchers and radiologists in developing more effective CAD systems for detecting lung cancer.

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“…Breast cancer is a prevalent and potentially life-threatening disease that requires accurate diagnosis and effective treatment strategies [4]. Data analysis techniques have advanced significantly in recent years which offers valuable insights into complex datasets [5]. Clustering is one of these techniques that has gained prominence in breast cancer research for its ability to uncover distinct patterns and relationships within the data, thereby aiding in improved decisionmaking and patient care.…”

Section: Introductionmentioning

confidence: 99%

Clustering performance using k-modes with modified entropy measure for breast cancer

Mahfuz,

Suhartanto,

Kusmardi

et al. 2023

IJEECS

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<span>Breast cancer is a serious disease that requires data analysis for diagnosis and treatment. Clustering is a data mining technique that is often used in breast cancer research to assess the level of malignancy at an early stage. However, clustering categorical data can be challenging because different levels in categorical variables can impact the clustering process. This research proposes a modified entropy measure (MEM) to enhance clustering performance. MEM aims to address the issue of distance-based measures in clustering categorical data. It is also a useful tool for assessing data loss in categorical clustering, which helps to understand the patterns and relationships by quantifying the information lost during clustering. An evaluation compares k-modes+MEM, k-means+MEM, DBSCAN+MEM, and affinity+MEM with conventional clustering algorithms. The assessment metrics of clustering accuracy, intra-cluster distance and fowlkes-mallow index (FMI) are employed to evaluate the algorithm performance. Experimental results show significant improvements. k-Modes+MEM algorithm achieves a reduction in average intra-cluster distance and outperforms other algorithms in accuracy, intra-cluster distance, and FMI. The proposed algorithm can be extended to heterogeneous datasets in various domains such as healthcare, finance, and marketing.</span>

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An extensible framework for recurrent breast cancer prognosis using deep learning techniques

Cited by 4 publications

References 19 publications

Predicting lung cancer risk using explainable artificial intelligence

Predicting lung cancer risk using explainable artificial intelligence

A review of convolutional neural network-based computer-aided lung nodule detection system

Clustering performance using k-modes with modified entropy measure for breast cancer

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