A Machine Learning Classification Technique for Predicting Prostate Cancer

B, Mohammed Ismail.; Alam, Mansoor; Tahernezhadi, M.; Vege, Hari Kiran; Rajesh, P.

doi:10.1109/eit48999.2020.9208240

Cited by 15 publications

(7 citation statements)

References 18 publications

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“…This research aims to examine the effectiveness of these machine learning algorithms in identifying distinguishing characteristics from prostate imaging data and precisely categorizing instances as either malignant or noncancerous. This study aims to develop a reliable and interpretable diagnostic tool for detecting prostate cancer by utilizing the advanced capabilities of deep learning architectures to capture complex patterns and structures in medical images, along with the versatility and robustness of random forest classification [ 19 ].…”

Section: The Design Methods and Proceduresmentioning

confidence: 99%

Prostate Cancer Detection from MRI Using Efficient Feature Extraction with Transfer Learning

Islam,

Imran,

Rabbi

2024

Prostate Cancer

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Prostate cancer is a common cancer with significant implications for global health. Prompt and precise identification is crucial for efficient treatment strategizing and enhanced patient results. This research study investigates the utilization of machine learning techniques to diagnose prostate cancer. It emphasizes utilizing deep learning models, namely VGG16, VGG19, ResNet50, and ResNet50V2, to extract relevant features. The random forest approach then uses these features for classification. The study begins by doing a thorough comparison examination of the deep learning architectures outlined above to evaluate their effectiveness in extracting significant characteristics from prostate cancer imaging data. Key metrics such as sensitivity, specificity, and accuracy are used to assess the models’ efficacy. With an accuracy of 99.64%, ResNet50 outperformed other tested models when it came to identifying important features in images of prostate cancer. Furthermore, the analysis of understanding factors aims to offer valuable insights into the decision-making process, thereby addressing a critical problem for clinical practice acceptance. The random forest classifier, a powerful ensemble learning method renowned for its adaptability and ability to handle intricate datasets, then uses the collected characteristics as input. The random forest model seeks to identify patterns in the feature space and produce precise predictions on the presence or absence of prostate cancer. In addition, the study tackles the restricted availability of datasets by utilizing transfer learning methods to refine the deep learning models using a small amount of annotated prostate cancer data. The objective of this method is to improve the ability of the models to generalize across different patient populations and clinical situations. This study’s results are useful because they show how well VGG16, VGG19, ResNet50, and ResNet50V2 work for extracting features in the field of diagnosing prostate cancer, when used with random forest’s classification abilities. The results of this work provide a basis for creating reliable and easily understandable machine learning-based diagnostic tools for detecting prostate cancer. This will enhance the possibility of an early and precise diagnosis in clinical settings such as index terms deep learning, machine learning, prostate cancer, cancer identification, and cancer classification.

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Section: The Design Methods and Proceduresmentioning

confidence: 99%

Prostate Cancer Detection from MRI Using Efficient Feature Extraction with Transfer Learning

Islam,

Imran,

Rabbi

2024

Prostate Cancer

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“…Naive Bayes belongs to the Bayes family as a probabilistic classifier. Decision Tree induction is an algorithm for the top-down recursive induction of tree [21], [22]. Support Vector Machine transforms the original training data into a higher dimension using a nonlinear mapping to figure out the best separating hyper plane.…”

Section: Experimental Methodologymentioning

confidence: 99%

Machine Learning and Statistical Analysis Techniques on Terrorism

Rajesh¹,

Donepudi²,

Alam³

et al. 2020

Fuzzy Systems and Data Mining VI

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Terrorism is a major issue facing the world today. It has negative impact on the economy of the nation suffering terrorist attacks from which it takes years to recover. Many developing countries are facing threats from terrorist groups and organizations. This paper examines various terrorist factors using data mining from the historical data to predict the terrorist groups most likely to attack a nation. In this paper we focus on sampled data primarily from India for the past two decades and also consider International database. To create meaningful insights, data mining, machine learning techniques and algorithms such as Decision Tree, Naïve Bayes, Support Vector Machine, Ensemble methods, Random Forest Classification are implemented to analyze comparative based classification results. Patterns and predictions are represented in the form of visualizations with the help of Python and Jupyter Notebook. This analysis will help to take appropriate preventive measures to stop Terrorism attacks and to increase investments, to grow the economy and tourism.

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“…The accuracy of this technique was 0.89%. In [7] A modi ed Logistic Regression (LR) classi er was proposed by authors for use in identifying prostate cancer risk factors. the factors linked to the clinical stage and neoplastic stage are used in the proposed categorization approach.…”

Section: Related Workmentioning

confidence: 99%

The Classification of the Prostate Cancer based on Transfer Learning Techniques

Khedr,

Wahed,

Al-Attar

et al. 2023

Preprint

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The most common cause of mortality worldwide and the most common male cancer is prostate cancer. According to the American Cancer Society. In the United States, there were 164,690 new instances of prostate cancer and at least 29,430 deaths from the disease in 2018, making up 9.5% of all new cancer cases. This will have a significant socioeconomic impact. Having the ability to determine the aggressiveness risk of confirmed prostate cancer could enhance the choice of proper treatment for individuals. This could lead to better outcomes, especially in terms of prostate cancer specific mortality. Deep learning-based significant prostate cancer classification has attracted a lot of attention because it may one day be used to support therapeutic decision-making. In this research we propose four models for classification the prostate cancer based on transfer learning algorithms (EfficentNet, DenseNet and Xception). We used two datasets for diagnosing prostate cancer. One of them is the standard dataset which consists of six grades of cancers and the other is a personal dataset from laboratory which is new dataset from treated patients at the hospital of Zagazig university. The results are obtained by using the standard dataset is approximately 93.6% accuracy using EfficientNetB7 pretrained model. The results of the created dataset are 97.08%, 97.98%, 98.87% by using EfiicientNet, DenseNet121 and Xception transfer learning models respectively. The results were compared with the state of arts, and it outperform all of them and also the model can be used in applications.

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A Machine Learning Classification Technique for Predicting Prostate Cancer

Cited by 15 publications

References 18 publications

Prostate Cancer Detection from MRI Using Efficient Feature Extraction with Transfer Learning

Prostate Cancer Detection from MRI Using Efficient Feature Extraction with Transfer Learning

Machine Learning and Statistical Analysis Techniques on Terrorism

The Classification of the Prostate Cancer based on Transfer Learning Techniques

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