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All Journal Bulletin of Electrical Engineering and Informatics Indonesian Journal of Electrical Engineering and Computer Science
Gulame, Mayuresh B.
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Electrical & Electronics Engineering

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Malignant thyroid lump multi classification by TIRADS using DBA with transfer learning Gulame, Mayuresh B.; Dixit, Vaibhav V.
Indonesian Journal of Electrical Engineering and Computer Science Vol 35, No 2: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v35.i2.pp996-1003

Abstract

Thyroid diseases have developed into significant illnesses in recent decades. These diseases affect the thyroid glands and are caused by elevated thyroid hormone levels or infections in the thyroid organs. It is challenging to resolve thyroid diagnosis using conventional parametric and nonparametric statistical techniques since it can be viewed as a classification problem. However, there are certain barriers in the manner of obtaining both efficacy and accuracy in thyroid nodule diagnosis. Deep learning (DL) and machine learning (ML) models have emerged as useful instruments for the diagnosis of sickness in the modern era. For the purpose of diagnosing and classifying thyroid diseases, this research introduces a novel deep belief network (DBF) with transfer learning, known as DBNTL. In this study, the pre-processed image was first pre-processed using a conventional multiresolution bilateral technique, and then it was subjected to a novel segmentation technique called fusion pooling integrated U-net segmentation. The DBN with transfer learning model is used to classify and grade malignant thyroid nodules in compliance with thyroid imaging-reporting-and-data-system (TIRADS) guidelines. In this model, the model's weights are obtained by transfer learning. A major metric for evaluating the efficacy of biological image processing applications, good sensitivity and specificity (97.28 and 97.22, respectively) were obtained for the recommended modes.
Transfer learning-based texture-enhanced convolutional neural networks over plant disease identification Thorat, Nilesh N.; Salunke, Mangesh D.; Pimpalkar, Aarti P.; Gulame, Mayuresh B.; Bbhagat, Babeetta; Hirve, Sumit; Saudagar, Saleha; Sanap, Madhura Eknath
Bulletin of Electrical Engineering and Informatics Vol 15, No 2: April 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i2.10510

Abstract

The global agricultural productivity and food security take serious threats due to the presence of plant diseases; thus, early and accurate diagnosis becomes the key to successful management of the disease. The traditional diagnosis techniques that rely on visual observation are time-based, subjective, and cannot be implemented on a large scale. Recent development in machine learning and computer vision provides possible solutions to automated plant disease detection. This paper suggests a plant disease identification with transfer learning (PDD-TL) model with the preprocessing, segmentation, feature extraction, and disease prediction phases. In the initial stages, median filtering is used to simplify the image quality, after which cells affected by the disease are segmented with the help of the integration of adaptive pixels in joint segmentation (IAPJS) algorithm. Multi-texton and pyramid histogram of oriented gradients (PHOG) are the discriminative features extracted. The classification of the disease is done with a new triple convolutional activation CNN with transfer learning (CNN-TCA-TL). In contrast to the current methods that use either a pure deep learning method or handcrafted features, the framework proposed explicitly employs both the use of texture descriptors and transferable deep representations, which retain fine-grained structural details. The experimental findings prove that CNN-TCA-TL has an accuracy of 0.92 which will prove that it is effective.
Co-Authors Bbhagat, Babeetta Dixit, Vaibhav V. Hirve, Sumit Pimpalkar, Aarti P. Salunke, Mangesh D. Sanap, Madhura Eknath Saudagar, Saleha Thorat, Nilesh N.