Artificial neural network in diagnosis of metastatic carcinoma in effusion cytology

Barwad, Adarsh; Dey, Pranab; Susheilia, Shaily

doi:10.1002/cyto.b.20632

Cited by 44 publications

(42 citation statements)

References 13 publications

(13 reference statements)

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“…In the field of medicine, ANN have been used in medical diagnosis, imaging, diagnosis of pathological specimens, waveform analysis (pattern recognition), outcome prediction (risk assessment), and instrument monitoring . ANNs have been applied occasionally in cytology such as in cervical cytology, effusion cytology, gastric, and thyroid cytology . However, ANNs have been rarely applied in breast cytology .…”

Section: Discussionmentioning

confidence: 99%

Artificial neural network in breast lesions from fine‐needle aspiration cytology smear

Subbaiah

Dey

Nijhawan

2013

Diagnostic Cytopathology

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Artificial neural networks (ANNs) are applied in engineering and certain medical fields. ANN has immense potential and is rarely been used in breast lesions. In this present study, we attempted to build up a complete robust back propagation ANN model based on cytomorphological data, morphometric data, nuclear densitometric data, and gray level co-occurrence matrix (GLCM) of ductal carcinoma and fibroadenomas of breast cases diagnosed on fine-needle aspiration cytology (FNAC). We selected 52 cases of fibroadenomas and 60 cases of infiltrating ductal carcinoma of breast diagnosed on FNAC by two cytologists. Essential cytological data was quantitated by two independent cytologists (SRM, PD). With the help of Image J software, nuclear morphomeric, densitometric, and GLCM features were measured in all the cases on hematoxylin and eosin-stained smears. With the available data, an ANN model was built up with the help of Neurointelligence software. The network was designed as 41-20-1 (41 input nodes, 20 hidden nodes, 1 output node). The network was trained by the online back propagation algorithm and 500 iterations were done. Learning was adjusted after every iteration. ANN model correctly identified all cases of fibroadenomas and infiltrating carcinomas in the test set. This is one of the first successful composite ANN models of breast carcinomas. This basic model can be used to diagnose the gray zone area of the breast lesions on FNAC. We assume that this model may have far-reaching implications in future.

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Section: Discussionmentioning

confidence: 99%

Artificial neural network in breast lesions from fine‐needle aspiration cytology smear

Subbaiah

Dey

Nijhawan

2013

Diagnostic Cytopathology

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“…Studies have also evaluated the utility of fractal dimensions in effusion cytology (Bhatia et al, 2009;Sharma et al, 2010). Recently artificial neural network has been used to detect metastatic carcinoma cells in effusion cytology (Barwad et al, 2012).…”

Section: 6919 Utility Of Nuclear Morphometry With Imagej In Effusionmentioning

confidence: 99%

Utility of Nuclear Morphometry in Effusion Cytology

Ambroise

Jothilingam²,

Ramdas³

2014

Asian Pacific Journal of Cancer Prevention

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Background: The cytological analysis of serous effusions is a common investigation and yields important diagnostic information. However, the distinction of reactive mesothelial cells from malignant cells can sometimes be difficult for the cytopathologist. Hence cost-effective ancillary methods are essential to enhance the accuracy of cytological diagnosis. The aim of this study was to examine the utility of nuclear morphometry in differentiating reactive mesothelial cells from malignant cells in effusion smears. Materials and Methods: Sixty effusion smears consisting of 30 effusions cytologically classified as malignant (adenocarcinomas) and 30 benign effusions showing reactive mesothelial cells were included in the study. ImageJ was used to measure the nuclear area, perimeter, maximal feret diameter, minimal feret diameter and the circularity. A total of ten representative cells were studied in each case. Results: Significant differences were found between benign and malignant effusions for the nuclear area, perimeter, maximal feret diameter and minimal feret diameter. No significant difference was found for circularity, a shape descriptor. Receiver operating characteristic (ROC) curve analysis revealed that nuclear area, perimeter, maximal feret diameter, and minimal feret diameter are helpful in discriminating benign and malignant effusions. Conclusions: Computerised nuclear morphometry is a helpful ancillary technique to distinguish benign and malignant effusions. ImageJ is an excellent cost effective tool with potential diagnostic utility in effusion cytology.

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“…NN follows analog and parallel computing system made up of simple processing elements that communicate through a rich set of interconnections with varying contributory weights. Artificial Neural Network (ANN), are synthetic nervous systems loosely inspired to simulate functions of human brain [26]. ANN attempts to abstract the complexity of biological nervous system so as to focus on what may hypothetically matter most from an information processing point of view.…”

Section: Neural Networkmentioning

confidence: 99%

A Genetic-Neuro-Fuzzy Inferential Technique for Diagnosis of Tuberculosis

Edafe

Mumini

Oluwarotimi³

2015

Proceedings of the 2015 Workshop on Pervasive Wireless Healthcare

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Tuberculosis (TB) is a social re-emerging infectious disease with medical implications throughout the globe. Despite efforts, the coverage of TB with HIV prevalence in Nigeria rose from 2.2% in 1991 to 22% in 2013 and the orthodox diagnosis methods available for TB diagnosis were been faced with a number of challenges which can increase the spread. This study proposes an inferential technique for intelligent diagnosis of TB using Genetic-Neuro-Fuzzy system as decision support platform. The system can aid medical practitioners in administering accurate, timely, and cost effective TB diagnosis. The system was evaluated with a case study of 10 patients from St. Francis Catholic Hospital, Delta State, Nigeria and results with sensitivity and accuracy of 60% and 70%, respectively, were observed.

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Artificial neural network in diagnosis of metastatic carcinoma in effusion cytology

Cited by 44 publications

References 13 publications

Artificial neural network in breast lesions from fine‐needle aspiration cytology smear

Artificial neural network in breast lesions from fine‐needle aspiration cytology smear

Utility of Nuclear Morphometry in Effusion Cytology

A Genetic-Neuro-Fuzzy Inferential Technique for Diagnosis of Tuberculosis

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