Artificial Neural Networks in Medical Diagnosis

By Y. Fukuoka. 2002

135 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Neurology

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Michele Nappi
2. H. Asadi
3. M. Lee
4. Chang Yi
5. Abid Ishaq
6. Kai Xu
7. Muhammad Umer
8. Seyedali Mirjalili

In Neurology

1. H. Asadi
2. M. Lee
3. G. Handelman
4. Amir Hossein Razavi
5. Shiv Ram Dubey
6. R. Chandra
7. H. Kok

Citations in the corpus

• Interpreting Deep Classifier by Visual Distillation of Dark Knowledge (2018, background): “Despite the many well-known successes of deep learning (Bishop, 2006; LeCun et al., 1998; Krizhevsky et al., 2012; Amato et al., 2013), deep classifiers often fall short on interpretability , which we take to mean whether a person can understand at a general level why the classifier makes the…”
• Improving the Prediction of Heart Failure Patients’ Survival Using SMOTE and Effective Data Mining Techniques (2021, background): “As such, proper treatment and diagnosis can save many lives [5].”
• Heart Disease Identification Method Using Machine Learning Classification in E-Healthcare (2020, background): “The associate editor coordinating the review of this manuscript and approvingitforpublicationwasNavaniethaKrishnarajKrishnarajRathinam. and proper treatment can save the lives of many people [5].”
• A Decade Survey of Content Based Image Retrieval Using Deep Learning (2020, background): “D data [35], [36], Convolutional Neural Networks (CNN) for image data [37], [38], and Reurrent Neural Networks (RNN) for time-series data [39], [40].”
• eDoctor: machine learning and the future of medicine (2018, methodology): “NN have been used in medical diagnosis with both conventional data sets and variables as well as data sets that use complex variables such as raw and transformed image data [65, 66], audio recordings of heart sounds and magnetic resonance spectroscopy, with significant results [67, 68].”