Skin Disease Classification Using CNN Algorithms

Agarwal, Raghav; Godavarthi, Deepthi

doi:10.4108/eetpht.9.4039

Cited by 8 publications

(4 citation statements)

References 26 publications

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“…In realworld applications, this model is useful since it can recognize newly discovered illness categories that were incorporated during the training phase. This technique provides a unique solution in the field and supports disease control management by making it easier to identify and categorize relevant diseases [21].…”

Section: Conclusion and Future Remarksmentioning

confidence: 99%

Multi-Class Kidney Abnormalities Detecting Novel System Through Computed Tomography

Pande,

Agarwal

2024

IEEE Access

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Impaired renal function poses a risk across all age groups. Because of the global shortage of nephrologists, the growing public health concern over renal failure, and technological improvements, there is a demand for an AI-driven system capable of autonomously detecting kidney abnormalities. Chronic kidney disease, commonly known as chronic renal failure, is characterized by a progressive decline in kidney function. Renal failure can be caused by a variety of reasons, including cysts, stones, and tumors. Chronic kidney disease may not have apparent symptoms at first, resulting in instances staying untreated until they reach an advanced state. Tumors are dense clumps of tissue that can cause direct injury to glands, spinal cells, and other organs. The presence of a substantial number of solids in the digestive tract causes kidney stone disease, also known as urolithiasis. This study used a deep learning model to detect kidney illnesses to solve the global scarcity of urologists. The project entailed obtaining and annotating a large dataset of 12,446 CT whole abdomen and urogram images, with an emphasis on kidney stones, cysts, and tumors, which are the most common types of renal illness. The dataset was divided into four categories: cyst, tumor, stone, and normal. Data was collected from several hospitals in the Dhaka area. This work presents an innovative and customizable platform for the clinical diagnosis of kidney diseases such as tumors, stones, and cysts. Our YOLOv8 model's enhanced accuracy opens up new possibilities for identifying kidney cysts, stones, and tumors. We used criteria like accuracy, precision, recall, F1 score, and specificity to evaluate its performance. The network attained an accuracy rate of 82.52%, 85.76% precision, 75.28% recall, 75.72% F1 score, and 93.12% specificity.

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Section: Conclusion and Future Remarksmentioning

confidence: 99%

Multi-Class Kidney Abnormalities Detecting Novel System Through Computed Tomography

Pande,

Agarwal

2024

IEEE Access

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“…The transfer learning model VGG19 is recommended for the diagnosis of Demented, Non-Demented, Moderately Demented, or Very Mildly Demented brain disorders to attain high classification accuracy [17]. To complete the training and testing process, we require a large enough dataset.…”

Section: Conclusion and Future Remarkmentioning

confidence: 99%

Comparative Analysis of Deep Learning Models for Multiclass Alzheimer’s Disease Classification

Agarwal,

Sathwik,

Godavarthi

et al. 2023

EAI Endorsed Trans Perv Health Tech

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INTRODUCTION: The terrible neurological condition is known Worldwide; millions of individuals are affected with Alzheimer's disease (AD). Effective treatment and management of AD depend on early detection and a precise diagnosis. An effective method for identifying anatomical and functional abnormalities in the brain linked to AD is magnetic resonance imaging (MRI). OBJECTIVES: However, manual MRI scan interpretation requires a lot of time and is inconsistent between observers. The automated analysis of MRI images for AD identification and diagnosis using deep learning techniques has shown promise. METHODS: In this paper, we present a convolutional neural network (CNN)-based deep learning model for automatically classifying MRI images for Alzheimer's (AD) and a healthy control group. A huge dataset of MRI scans was used to train the CNN, which distinguished between AD and healthy control groups with excellent accuracy. RESULTS: Additionally, we looked into how transfer learning may be used to enhance pre-trained models and boost CNN performance. We discovered that transfer learning considerably increased the model's accuracy and decreased overfitting. Our findings show that MRI scans may be used to precisely detect and diagnose AD utilizing approaches to deep learning and machine learning. CONCLUSION: These techniques may improve the efficiency and accuracy of AD diagnosis and enable early disease identification, resulting in better AD management and therapy.

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“…This section presents a detailed breakdown of the estimated costs associated with implementing the intelligent Raspberry Pi-based parking slot identification system. By accurately assessing the expenses, we aim to provide an understanding of the financial implications and feasibility of the proposed system [11]. This section also presents a comparative analysis of the cost associated with implementing an intelligent Raspberry Pi-based parking slot identification system in contrast to existing systems.…”

Section: Cost and Comparison With Existing Systemsmentioning

confidence: 99%

Intelligent Raspberry-Pi-Based Parking Slot Identification System

Agarwal,

Sharma,

Singh

et al. 2023

EAI Endorsed Trans Ind Net Intel Syst

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A growing population necessitates more transportation, which pressures car parking spots. Parking is a problem for public places in cities, such as theatres, malls, parks, and temples. Even though several techniques have been suggested in publications, manual parking systems are still used in most places. For large locations where it is challenging to find open spaces, traditional parking arrangements need to be more archaic and convoluted. This might lead to heavy traffic, minor mishaps, and widespread accidents. In the modern era of sophisticated parking management systems, an automatic parking spot-detecting system has been introduced in an innovative format. Experts in computer vision are drawn to this emerging field to contribute. The system could tell if the automobile was fully or partially parked. Neither during the process nor afterward, human oversight is required. As parking management enters the modern era, computer vision is becoming increasingly critical. The parking system will not only make it easier for drivers to identify parking spaces but also enhance parking administration and monitoring. Vehicles will be able to observe available parking spots due to technology that monitors parking spaces. India and other emerging nations, as well as industrialized ones, have recently shown interest in smart cities. This article's smart auto parking system was conceived and implemented utilizing a Raspberry Pi and cameras placed in various parking spaces. Using a website and an Android app, this project creates and deploys a real-time system that enables vehicles to efficiently find and reclaim open parking spaces.

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Skin Disease Classification Using CNN Algorithms

Cited by 8 publications

References 26 publications

Multi-Class Kidney Abnormalities Detecting Novel System Through Computed Tomography

Multi-Class Kidney Abnormalities Detecting Novel System Through Computed Tomography

Comparative Analysis of Deep Learning Models for Multiclass Alzheimer’s Disease Classification

Intelligent Raspberry-Pi-Based Parking Slot Identification System

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