Jasmin Pemeena Priyadarisini M

Bio: Jasmin Pemeena Priyadarisini M is an academic researcher. The author has contributed to research in topics: Digital image processing & Image formation. The author has an hindex of 1, co-authored 1 publications receiving 1 citations.

A Review on Image Processing Applications in Medical Field

Abstract: Objective: Biomedical signal/image processing and the related imaging modalities is a very vast growing and upcoming field This paper presents the promising image processing techniques used in medical field Methods: Application of image processing techniques has played a vital role in assisting the surgeons and physicians in diagnosing the diseases and performing the surgeries for the patients Clinical medical devices has erupted through combination of hardware and image processing techniques which has a giant leap in medical field Results: Biomedical signals fetching, image formation, processing of image, and image display for medical/clinical diagnosis based on extracted features from the signal (1D), images (2D) and Video (3D) Image processing has proved its significance in medical analysis and health care Conclusion: The imaging modalities play a vital role in acquisition of signals and images from human body which involves invasive and non-invasive methods

CNN Comparative Study for Apple Quality Classification

Abstract: A reliable and efficient automated fruit grading process is needed to meet the market's increased demand for good quality fruits. Automated systems based on image processing technology are being developed to reduce reliance on manual expertise, which is often time-consuming, expensive, and biased. The propose of this paper is to construct and evaluate different types of CNN models, including conventional CNN, GoogLeNet and AlexNet, for categorising fresh and rotten appearances using a dataset of apple images. All the datasets had performed the operations of pre-processing steps with scaling, rotating, and cropping. All models were trained and tested using the datasets provided by the KAGGLE network. The accuracy and loss performance of all the models are measured. The result shows that the GoogLeNet achieved 100% accuracy, which has better performance compared to the AlexNet and conventional CNN. Hence, the GoogLeNet model has the potential for integration into an automatic fruit grading system.

CNN Comparative Study for Apple Quality Classification

Abstract: A reliable and efficient automated fruit grading process is needed to meet the market's increased demand for good quality fruits. Automated systems based on image processing technology are being developed to reduce reliance on manual expertise, which is often time-consuming, expensive, and biased. The propose of this paper is to construct and evaluate different types of CNN models, including conventional CNN, GoogLeNet and AlexNet, for categorising fresh and rotten appearances using a dataset of apple images. All the datasets had performed the operations of pre-processing steps with scaling, rotating, and cropping. All models were trained and tested using the datasets provided by the KAGGLE network. The accuracy and loss performance of all the models are measured. The result shows that the GoogLeNet achieved 100% accuracy, which has better performance compared to the AlexNet and conventional CNN. Hence, the GoogLeNet model has the potential for integration into an automatic fruit grading system.

Blood image analysis to detect malaria using filtering image edges and classificatio

Abstract: Malaria is a most dangerous mosquito borne disease and its infection spread through the infected mosquito. It especially affects the pregnant females and Children less than 5 years age. Malarial species commonly occur in five different shapes, Therefore, to avoid this crucial disease the contemporary researchers have proposed image analysis based solutions to mitigate this death causing disease. In this work, we propose diagnosis algorithm for malaria which is implemented for testing and evaluation in Matlab. We use Filtering and classification along with median filter and SVM classifier. Our proposed method identifies the infected cells from rest of blood images. The Median filtering smoothing technique is used to remove the noise. The feature vectors have been proposed to find out the abnormalities in blood cells. Feature vectors include (Form factor, measurement of roundness, shape, count total number of red cells and parasites). Primary aim of this research is to diagnose malaria by finding out infected cells. However, many techniques and algorithm have been implemented in this field using image processing but accuracy is not up to the point. Our proposed algorithm got more efficient results along with high accuracy as compared to NCC and Fuzzy classifier used by the researchers recently.

Fabry disease automatic recognition through image processing

Abstract: Image processing algorithms are widely used nowadays, finding employment in the photographic field, in facial recognition, up to clinical applications. Diagnostic processes are based on digital images obtained using radiological tools capable of representing regions of the human body on 2D or 3D matrices. Image processing techniques are fundamental in order to obtain good results in terms of resolution, contrast and for a correct reconstruction of the image. Histological examinations are important for diagnosing pathologies too, analysing microscopic images of biological tissue. This is the case with Fabry disease, whose symptoms and complications can be severe (as kidney damage, heart attacks and strokes) with important effects on the quality of life of those affected. This work shows image processing algorithms as useful diagnostic support tools, capable to segment cellular tissue and to identify glycolipid bodies, typical of Fabry disease, in cellular tissue digital optical microscopic images. The obtained results are the number of intracellular accumulation structures with an estimated error lower than 10%, and the ratio of the areas of cellular tissue and glycosphingolipid accumulations, which constitute quantitative parameters for the diagnosis. Tools execution times are very low, from few seconds up to 10 minutes, with a substantial time saving compared to manual analysis. Also a mobile version of the algorithms is developed, as important application in the field of telepathology.