AI Medical Compendium Journal:
Diagnostic cytopathology

Showing 1 to 7 of 7 articles

Diagnostic utility of transfer learning by using convolutional neural network for cytological diagnosis of malignant effusions.

Diagnostic cytopathology
INTRODUCTION: Cytological analysis of effusion specimens provides critical information regarding the diagnosis and staging of malignancies, thus guiding their treatment and subsequent monitoring. Keeping in view the challenges encountered in the morp...

Improving cervical cancer classification in PAP smear images with enhanced segmentation and deep progressive learning-based techniques.

Diagnostic cytopathology
OBJECTIVE: Cervical cancer, a prevalent and deadly disease among women, comes second only to breast cancer, with over 700 daily deaths. The Pap smear test is a widely utilized screening method for detecting cervical cancer in its early stages. Howeve...

Automated classification of cancer from fine needle aspiration cytological image use neural networks: A meta-analysis.

Diagnostic cytopathology
BACKGROUND: The role of retrospective analysis has been evolved greatly in cancer research. We undertook this meta-analysis to evaluate the diagnostic value of Neural networks (NNs) in Fine needle aspiration cytological (FNAC) image of cancer.

Discriminant analysis and interpretation of nuclear chromatin distribution and coarseness using gray-level co-occurrence matrix features for lobular endocervical glandular hyperplasia.

Diagnostic cytopathology
BACKGROUND: Lobular endocervical glandular hyperplasia (LEGH) is a disease considered to be the origin of tumorigenesis of minimal deviation adenocarcinoma, which has characteristic expression in the gastric pyloric mucosa. It is difficult to diagnos...

Adaptation of CytoProcessor for cervical cancer screening of challenging slides.

Diagnostic cytopathology
BACKGROUND: Current automated cervical cytology screening systems require purchase of a dedicated preparation machine and use of a specific staining protocol. CytoProcessor (DATEXIM, Caen, France) is a new automated system, designed to integrate seam...

Artificial neural network model to distinguish follicular adenoma from follicular carcinoma on fine needle aspiration of thyroid.

Diagnostic cytopathology
BACKGROUND: To distinguish follicular adenoma (FA) and follicular carcinoma (FC) of thyroid in fine needle aspiration cytology (FNAC) is a challenging problem.