Efficient Development of Supervised Learning Algorithm for kidney Stone Prediction

Kolli, Chandra Sekhar; Raghunath, Mutkule Prasad; Meenakshi, S.; Maheswari, K.; Britto, Christopher Francis; Kushwaha, Sumit

doi:10.1109/icict54344.2022.9850573

Cited by 4 publications

(2 citation statements)

References 16 publications

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“…EEG signals were the main focus of this research. Brain activity can be tracked non-invasively with EEG [9]. Brain-computer interface (BCI) concepts are used to analyze EEG signals.…”

Section: International Journal On Recent and Innovation Trends In Com...mentioning

confidence: 99%

“…The DTFT samples are used as coefficients of complex sinusoids at the relevant DTFT frequencies in an inverse DFT, which is a Fourier series. The graph is also shown below [9] Figure . 8 Amplitude vs frequency Spectrum Here too, a specific threshold value could not be obtained. The main reason for the failure of this method was the inability to find a threshold value.…”

Section: Neurosky Mindwave Headsetmentioning

confidence: 99%

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Mind Control Robotic Arm: Augmentative and Alternative Communication in the Classroom Environment

Murthy,

H.,

Mirajkar

et al. 2023

IJRITCC

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In recent years, technological advancements have greatly benefited the field of prosthetics. A large number of disabled people depend on prosthetics because they are an important technology. In order to provide augmentative and alternative methods of communication to these disabled people with various neuromuscular disorders, we must make sure they are provided with appropriate equipment to express themselves. Different types of arms are evaluated under robotic technology in terms of resistance, usability, flexibility, cost, and potential (such as robotic, surgical, bionic, prosthetic, and static arms). The main problems with these techniques are their high cost, the difficulty of installing and maintaining them, and the possibility of requiring surgery may arise. As a result, this paper is going to provide a description of the idea for combining an EEG controlled smart prosthetic arm with a smart robotic hand. An electrode headset is used to capture the signals from the robotic hand in order to control the device. Creating a robot arm that can help disabled people lead a more independent life is the main objective of this paper.

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“…EEG signals were the main focus of this research. Brain activity can be tracked non-invasively with EEG [9]. Brain-computer interface (BCI) concepts are used to analyze EEG signals.…”

Section: International Journal On Recent and Innovation Trends In Com...mentioning

confidence: 99%

Section: Neurosky Mindwave Headsetmentioning

confidence: 99%

Mind Control Robotic Arm: Augmentative and Alternative Communication in the Classroom Environment

Murthy,

H.,

Mirajkar

et al. 2023

IJRITCC

View full text Add to dashboard Cite

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A Deep Learning Framework for Kidney Stone Prediction

Yamuna,

Stalin Babu,

Vijay Kumar

et al. 2024

Lecture Notes in Electrical Engineering

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On the In Vivo Recognition of Kidney Stones Using Machine Learning

Lopez-Tiro,

Estrade,

Hubert

et al. 2024

IEEE Access

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Determining the type of kidney stones allows urologists to prescribe a treatment to avoid the recurrence of renal lithiasis. An automated in-vivo image-based classification method would be an important step towards an immediate identification of the kidney stone type required as a first phase of the diagnosis. In the literature, it was shown on ex-vivo data (i.e., in very controlled scene and image acquisition conditions) that an automated kidney stone classification is indeed feasible. This pilot study compares the kidney stone recognition performances of six shallow machine learning methods and three deep-learning architectures which were tested with in-vivo images of the four most frequent urinary calculus types acquired with an endoscope during standard ureteroscopies. This contribution details the construction of an in-vivo dataset of endoscopic images with four of the most recurrent classes in clinical practice. It also describes the design, implementation, and results of the classifiers (shallow machine learning and deep learning-based methods) of kidney stones. Even if the best results were obtained by the InceptionV3 architecture (weighted accuracy, precision, recall and F1-Score of 97 ± 03%, 97 ± 03%, 98 ± 04%, and 98 ± 03%, respectively), it is also shown that choosing an appropriate colour space and texture features allows a shallow machine learning method to approach closely the performances of the most promising deep-learning methods (the XGBoost classifier led to weighted accuracy, precision, recall and F1-Score values of 96 ± 14%).

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Efficient Development of Supervised Learning Algorithm for kidney Stone Prediction

Cited by 4 publications

References 16 publications

Mind Control Robotic Arm: Augmentative and Alternative Communication in the Classroom Environment

Mind Control Robotic Arm: Augmentative and Alternative Communication in the Classroom Environment

A Deep Learning Framework for Kidney Stone Prediction

On the In Vivo Recognition of Kidney Stones Using Machine Learning

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