A comprehensive review on breast cancer detection, classification and segmentation using deep learning
The incidence and mortality rate of Breast Cancer (BC) are global problems for women, with
over 2.1 million new diagnoses each year worldwide. There is no age range, race, or …
over 2.1 million new diagnoses each year worldwide. There is no age range, race, or …
A systematic review on biomarker identification for cancer diagnosis and prognosis in multi-omics: from computational needs to machine learning and deep learning
Biomarkers, also known as biological markers, are substances like transcripts,
deoxyribonucleic acid (DNA), genes, proteins, and metabolites that indicate whether a …
deoxyribonucleic acid (DNA), genes, proteins, and metabolites that indicate whether a …
A comprehensive investigation of multimodal deep learning fusion strategies for breast cancer classification
In breast cancer research, diverse data types and formats, such as radiological images,
clinical records, histological data, and expression analysis, are employed. Given the intricate …
clinical records, histological data, and expression analysis, are employed. Given the intricate …
Computational techniques and tools for omics data analysis: state-of-the-art, challenges, and future directions
The heterogeneous and high-dimensional nature of omics data presents various challenges
in gaining insights while analysis. In the era of big data, omics data is available as genome …
in gaining insights while analysis. In the era of big data, omics data is available as genome …
HFBSurv: hierarchical multimodal fusion with factorized bilinear models for cancer survival prediction
Motivation Cancer survival prediction can greatly assist clinicians in planning patient
treatments and improving their life quality. Recent evidence suggests the fusion of …
treatments and improving their life quality. Recent evidence suggests the fusion of …
eDiaPredict: an ensemble-based framework for diabetes prediction
Medical systems incorporate modern computational intelligence in healthcare. Machine
learning techniques are applied to predict the onset and reoccurrence of the disease …
learning techniques are applied to predict the onset and reoccurrence of the disease …
CAMR: cross-aligned multimodal representation learning for cancer survival prediction
Motivation Accurately predicting cancer survival is crucial for hel** clinicians to plan
appropriate treatments, which largely improves the life quality of cancer patients and spares …
appropriate treatments, which largely improves the life quality of cancer patients and spares …
IoTPulse: machine learning-based enterprise health information system to predict alcohol addiction in Punjab (India) using IoT and fog computing
This paper proposes IoT-based an enterprise health information system called IoTPulse to
predict alcohol addiction providing real-time data using machine-learning in fog computing …
predict alcohol addiction providing real-time data using machine-learning in fog computing …
[HTML][HTML] Publicly available datasets of breast histopathology H&E whole-slide images: A sco** review
Advancements in digital pathology and computing resources have made a significant impact
in the field of computational pathology for breast cancer diagnosis and treatment. However …
in the field of computational pathology for breast cancer diagnosis and treatment. However …
Survival risk prediction of esophageal cancer based on the kohonen network clustering algorithm and kernel extreme learning machine
Y Wang, H Wang, S Li, L Wang - Mathematics, 2022 - mdpi.com
Accurate prediction of the survival risk level of patients with esophageal cancer is significant
for the selection of appropriate treatment methods. It contributes to improving the living …
for the selection of appropriate treatment methods. It contributes to improving the living …