Generative AI and Deep Learning in Medical Diagnostics

A special issue of Diagnostics (ISSN 2075-4418). This special issue belongs to the section "Machine Learning and Artificial Intelligence in Diagnostics".

Deadline for manuscript submissions: 31 October 2025 | Viewed by 2649

Special Issue Editor

Special Issue Information

Dear Colleagues,

Generative AI has immense potential to revolutionize medical diagnosis and prognosis by enhancing medical image analysis, aiding in diagnosis, and improving patient outcomes. This Special Issue will present up-to-date knowledge and examples of the use of generative AI in a wide range of applications within medical diagnosis and prognosis.

Dr. Steven L. Fernandes
Guest Editor

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Keywords

  • generative AI
  • medical diagnostics
  • medical imaging processing and analysis
  • deep learning
  • machine learning

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Published Papers (1 paper)

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Research

19 pages, 3213 KiB  
Article
An Empirical Evaluation of a Novel Ensemble Deep Neural Network Model and Explainable AI for Accurate Segmentation and Classification of Ovarian Tumors Using CT Images
by Ashwini Kodipalli, Steven L. Fernandes and Santosh Dasar
Diagnostics 2024, 14(5), 543; https://doi.org/10.3390/diagnostics14050543 - 4 Mar 2024
Cited by 1 | Viewed by 1720
Abstract
Ovarian cancer is one of the leading causes of death worldwide among the female population. Early diagnosis is crucial for patient treatment. In this work, our main objective is to accurately detect and classify ovarian cancer. To achieve this, two datasets are considered: [...] Read more.
Ovarian cancer is one of the leading causes of death worldwide among the female population. Early diagnosis is crucial for patient treatment. In this work, our main objective is to accurately detect and classify ovarian cancer. To achieve this, two datasets are considered: CT scan images of patients with cancer and those without, and biomarker (clinical parameters) data from all patients. We propose an ensemble deep neural network model and an ensemble machine learning model for the automatic binary classification of ovarian CT scan images and biomarker data. The proposed model incorporates four convolutional neural network models: VGG16, ResNet 152, Inception V3, and DenseNet 101, with transformers applied for feature extraction. These extracted features are fed into our proposed ensemble multi-layer perceptron model for classification. Preprocessing and CNN tuning techniques such as hyperparameter optimization, data augmentation, and fine-tuning are utilized during model training. Our ensemble model outperforms single classifiers and machine learning algorithms, achieving a mean accuracy of 98.96%, a precision of 97.44%, and an F1-score of 98.7%. We compared these results with those obtained using features extracted by the UNet model, followed by classification with our ensemble model. The transformer demonstrated superior performance in feature extraction over the UNet, with a mean Dice score and mean Jaccard score of 0.98 and 0.97, respectively, and standard deviations of 0.04 and 0.06 for benign tumors and 0.99 and 0.98 with standard deviations of 0.01 for malignant tumors. For the biomarker data, the combination of five machine learning models—KNN, logistic regression, SVM, decision tree, and random forest—resulted in an improved accuracy of 92.8% compared to single classifiers. Full article
(This article belongs to the Special Issue Generative AI and Deep Learning in Medical Diagnostics)
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