Breast Cancer Prediction Empowered with Fine-Tuning

Nasir, Muhammad Umar; Ghazal, Taher M.; Khan, Muhammad Adnan; Zubair, Muhammad; Rahman, Atta; Ahmed, Rashad; Hamadi, Hussam Al; Yeun, Chan Yeob

doi:10.1155/2022/5918686

Cited by 38 publications

(5 citation statements)

References 32 publications

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“…In the last phase, which is known as the testing phase, import blood samples from the cloud, import the best-performed trained model from the model secluded, and apply the testing process to predict the cancerous white blood cells. Finally, the proposed framework used numerous statistical matrices [37][38][39][40][41][42][43], e.g., classifcation accuracy (CA), negative predicted value (NPV), sensitivity, specifcity, f1-score, missclassifcation rate (MCR), positive predicted value (PPV), likelihood positive ratio (LPR), false negative rate (FNR), likelihood negative ratio (LNR), false positive rate (FPR), and Fowlkes Mallows index (FMI), all statistical matrix equations are given as follows:…”

Section: Methodsmentioning

confidence: 99%

Hematologic Cancer Detection Using White Blood Cancerous Cells Empowered with Transfer Learning and Image Processing

Nasir

Khan

et al. 2023

Journal of Healthcare Engineering

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Lymphoma and leukemia are fatal syndromes of cancer that cause other diseases and affect all types of age groups including male and female, and disastrous and fatal blood cancer causes an increased savvier death ratio. Both lymphoma and leukemia are associated with the damage and rise of immature lymphocytes, monocytes, neutrophils, and eosinophil cells. So, in the health sector, the early prediction and treatment of blood cancer is a major issue for survival rates. Nowadays, there are various manual techniques to analyze and predict blood cancer using the microscopic medical reports of white blood cell images, which is very steady for prediction and causes a major ratio of deaths. Manual prediction and analysis of eosinophils, lymphocytes, monocytes, and neutrophils are very difficult and time-consuming. In previous studies, they used numerous deep learning and machine learning techniques to predict blood cancer, but there are still some limitations in these studies. So, in this article, we propose a model of deep learning empowered with transfer learning and indulge in image processing techniques to improve the prediction results. The proposed transfer learning model empowered with image processing incorporates different levels of prediction, analysis, and learning procedures and employs different learning criteria like learning rate and epochs. The proposed model used numerous transfer learning models with varying parameters for each model and cloud techniques to choose the best prediction model, and the proposed model used an extensive set of performance techniques and procedures to predict the white blood cells which cause cancer to incorporate image processing techniques. So, after extensive procedures of AlexNet, MobileNet, and ResNet with both image processing and without image processing techniques with numerous learning criteria, the stochastic gradient descent momentum incorporated with AlexNet is outperformed with the highest prediction accuracy of 97.3% and the misclassification rate is 2.7% with image processing technique. The proposed model gives good results and can be applied for smart diagnosing of blood cancer using eosinophils, lymphocytes, monocytes, and neutrophils.

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Section: Methodsmentioning

confidence: 99%

Hematologic Cancer Detection Using White Blood Cancerous Cells Empowered with Transfer Learning and Image Processing

Nasir

Khan

et al. 2023

Journal of Healthcare Engineering

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“…After training all of the models, the best-trained model is imported to the secure, private blockchain cloud Z for the further testing process. In the final phase, i.e., the testing phase, the following steps are performed: (1) import the best-trained model from private cloud Z and import the testing data samples from a private cloud of data; (2) apply the testing techniques and predict the kidney cancer (kidney cancer data samples are categorized into three predictive classes: grade 0, grade 1, and grade 2); (3) apply various statistical matrixes, e.g., Classification Accuracy (CA), Miss-Classification Rate (MCR), sensitivity, specificity, f1-score, Positive Predicted Value (PPV), Negative Predicted Value (NPV), False Positive Rate (FPR), False Negative Rate (FNR), Likelihood Positive Ratio (LPR), Likelihood Negative Ratio (LNR) and Fowlkes Mallows Index (FMI), to check the performance of the proposed framework (all equations are explained below) [ 28 , 29 , 30 , 31 , 32 , 33 , 34 ]. After the kidney cancer prediction, patients can consult with doctors for early therapy.…”

Section: Methodsmentioning

confidence: 99%

Kidney Cancer Prediction Empowered with Blockchain Security Using Transfer Learning

Nasir

Zubair

Ghazal

et al. 2022

Sensors

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Kidney cancer is a very dangerous and lethal cancerous disease caused by kidney tumors or by genetic renal disease, and very few patients survive because there is no method for early prediction of kidney cancer. Early prediction of kidney cancer helps doctors start proper therapy and treatment for the patients, preventing kidney tumors and renal transplantation. With the adaptation of artificial intelligence, automated tools empowered with different deep learning and machine learning algorithms can predict cancers. In this study, the proposed model used the Internet of Medical Things (IoMT)-based transfer learning technique with different deep learning algorithms to predict kidney cancer in its early stages, and for the patient’s data security, the proposed model incorporates blockchain technology-based private clouds and transfer-learning trained models. To predict kidney cancer, the proposed model used biopsies of cancerous kidneys consisting of three classes. The proposed model achieved the highest training accuracy and prediction accuracy of 99.8% and 99.20%, respectively, empowered with data augmentation and without augmentation, and the proposed model achieved 93.75% prediction accuracy during validation. Transfer learning provides a promising framework with the combination of IoMT technologies and blockchain technology layers to enhance the diagnosing capabilities of kidney cancer.

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“…This approach employs CNNs to predict the longevity of breast cancer patients by analyzing multi-modal data. A ne-tuning model [25] was recommended to predict breast cancer from MRI scans. This model employs a pretrained deep learning network of AlexNet for ne-tuning to identify the sick and healthy regions of breast cancer more e ciently.…”

Section: Cancer Prediction Using Deep Learning Techniquesmentioning

confidence: 99%

Applied Machine Intelligence in Cancer Detection and Proactive Prediction

2024

Preprint

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Cancer is one of the fatal diseases diagnosed among people globally. Proactive cancer prediction is a complex and challenging research problem that requires the potential analysis of large multimodal data, including patient medical records, numeric and imaging data, and genomic data. According to the World Health Organization (WHO) Global Health Estimate (GHE)-2020, which was conducted over 180 countries, non-communicable disease categories include multiple subcategories, of which all cancer types account for 16.8% of global deaths, making it the second leading cause of death after cardiovascular diseases [1]. Accounting for 2020, it is estimated that nearly 10 million people died from cancer, which is approximately one in six deaths. According to the International Agency of Research on Cancer (IARC), it is predicted that the approximate incidence and mortality counts will rise to 29.5 million and 16.4 million, respectively, by the year 2040 [2].

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Breast Cancer Prediction Empowered with Fine-Tuning

Cited by 38 publications

References 32 publications

Hematologic Cancer Detection Using White Blood Cancerous Cells Empowered with Transfer Learning and Image Processing

Hematologic Cancer Detection Using White Blood Cancerous Cells Empowered with Transfer Learning and Image Processing

Kidney Cancer Prediction Empowered with Blockchain Security Using Transfer Learning

Applied Machine Intelligence in Cancer Detection and Proactive Prediction

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