No abstract

BACKGROUNDThe use of Fine Needle Aspiration Cytology (FNAC) in the diagnosis of salivary gland lesions has seen a significant rise. In majority of cases, FNAC is helpful in differentiating between benign and malignant lesions. However, many a times it becomes quite challenging to give a precise diagnosis. The sensitivity of diagnosis of malignant lesions is high, though specificity is low. Aims-Objectives-1. To study the cytomorphology of salivary gland lesions. 2. To examine the sensitivity, specificity and diagnostic accuracy of FNAC of salivary gland lesions by cytohistopathological correlation and to identify the discrepancies that contri bute to false diagnosis. MATERIALS AND METHODSFifty one salivary gland FNAC cases received at the Department of Pathology, A. J. Institute of Medical Sciences were reviewed to identify the cytological characteristics. A cytohistopathological correlation was done wherever available and discordant cases were analysed. RESULTSIn the present study, out of 51 cases majority were males (33 cases, 65%). Twenty (40%) were diagnosed non-neoplastic, eighteen (35%) were rendered benign, while thirteen (25%) were malignant on cytology. Pleomorphic adenoma was the most common benign tumour (77% of the benign lesions) and mucoepidermoid carcinoma was the most common malignant tumour (30% of the malignant lesions). Biopsy confirmation of the cytological diagnoses were available in twenty one (41%) cases; 42.8% of the total cases showed discordant diagnosis. CONCLUSIONFNAC is a reliable tool in distinguishing benign and malignant salivary gland lesions in the hands of an experienced cytopathologist. A specific cytological diagnosis is often possible. However, due to the diagnostic pitfalls in FNAC, a biopsy confirmation may be necessary.

Plant disease is an on-going challenge for the farmers and it has been one of the major threats to the income and the food security. This project is used to classify plant leaf into diseased and healthy leaf,to improve the quality and quantity of agricultural production in the country. The innovative technology that helps in improve the quality and quantity in the agricultural field is the smart farming system. It represented the modern method that provides cost-effective disease detection and deep learning with convolutional neural networks (CNNs) has achieved large successfulness in the categorisation of different plant leaf diseases. CNN reads a really very larger picture in a simple way. CNN nearly utilised to examine visual imagery and are frequently working behind the scenes in image classification. To extract the general features and then classify them under multiple based upon the features detected. This project will help the farmers financially in increasing the production of the crop yield as well as the overall agricultural production. The paper reviews the expected methods of plant leaf disease detection system that facilitates the advancement in agriculture. It includes various phases such as image preprocessing, image classification, feature extraction and detecting healthy or diseased.

Trichilemmal tumours are the tumours derived from the hair follicles. Malignant counterpart of these tumours is rarely encountered when compared to the benign ones. We present a case of recurrent malignant proliferating trichilemmal tumour in an elderly male