Tumor Detection Based on 3D Segmentation Using Region of Interest

Rajesh, T.; Shaila, S. G.; Koppal, Lavanya B.

doi:10.1201/9781003190011-8

Cited by 2 publications

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“…Polepogu Rajesh, Kunduru Umamaheswari [19]: Objective of this paper is to provide a complete solution to diagnosis the suspicious thyroid region in the thyroid gland. An advanced method of completely automatic identification of thyroid nodule is proposed in this paper.…”

Section: Classificationmentioning

confidence: 99%

The Role of Machine Learning in Thyroid Cancer Diagnosis

Kate¹,

Kale²

2023

Advances in Computer Science Research

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Thyroid cancer occurs in the thyroid gland cells. This is butterfly shaped gland which is situated at lower part of neck. Thyroid cancer might not cause any symptoms initially. But as the cancer grows, it causes pain and swelling in neck. To detect and classify abnormalities of the thyroid gland Ultrasound imaging is mostly used. Computer aided diagnosis (CAD) help healthcare sector to increase the diagnosis accuracy and to reduce biopsy ratio. Machine learning techniques play a vital role in diagnosing diseases from the medical database. Various diseases can be predicted early using machine learning techniques. In this study, many previous research works are reviewed which use machine learning techniques to predict thyroid cancer. Machine learning classification techniques like Decision tree Classification, Random Forest Classification, Naïve Bayes, Kernel SVM, K-Nearest Neighbours, Support vector Machines (SVM), Logistic regression etc. are reviewed from the research papers. A short description about machine learning and segmentation is also presented.

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Section: Classificationmentioning

confidence: 99%

The Role of Machine Learning in Thyroid Cancer Diagnosis

Kate¹,

Kale²

2023

Advances in Computer Science Research

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A Novel Framework for Grading of Heart Attack

Rajesh¹,

RenukaDevi²,

Shaila³

et al. 2023

Advances in Computer Science Research

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In recent years, cardiovascular diseases have become common. Serious health problems arise in the human body as a result of an unhealthy lifestyle, the use of alcohol and tobacco, obesity, stress, and dietary changes. This has complicated surgeons' ability to diagnose heart failure at the right time. A heart attack occurs when the blood flow that brings oxygen to the heart muscle is severely condensed or cut off completely. ECG is a medical test that is used in the detection of heart attacks in patients. Extracting the essential features from ECG images is the most crucial task. The key features are extracted using connected component analysis, hierarchical centroid, Hough line transform, and height and width. Various techniques like Fast Fourier Transform, Discrete Fourier Transform, Decision Tree and Principal Component Analysis are used to predict heart failure. In this model, we are going to examine ECG signal images and detect whether the person is prone to heart attack or not. A comparative study of different models showed that the proposed work enhanced the previous accuracy score in predicting heart failure using FFT.

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Tumor Detection Based on 3D Segmentation Using Region of Interest

Cited by 2 publications

References 0 publications

The Role of Machine Learning in Thyroid Cancer Diagnosis

The Role of Machine Learning in Thyroid Cancer Diagnosis

A Novel Framework for Grading of Heart Attack

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