Melanoma classification using convolutional neural network

Kumar, G. Kiran; Rani, D. Malathi; Gopalachari, M. Venu; Rakesh, S.; Ashraf, Syed

doi:10.1063/5.0162544

Cited by 3 publications

(1 citation statement)

References 18 publications

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“…To analyze images in our study, we utilized a Convolutional Neural Network (CNN) model [25]. The preprocessed image served as input for the CNN model, which consisted of an input layer, convolution layers, and a fully connected layer.…”

Section: Convolutional Neural Network For Glaucoma Detectionmentioning

confidence: 99%

Automated Glaucoma Detection in Retinal Fundus Images Using Machine Learning Models

Nagaratna P Hegde

2024

jes

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Glaucoma, a neurodegenerative eye disorder, stands as a major global health concern, ranking second in causing blindness. The urgency for early detection is paramount to mitigate its irreversible effects on vision. This research presents an innovative model adept at analyzing retinal fundus images, aiming to assist ophthalmologists in early diagnosis. By harnessing the capabilities of Convolutional Neural Networks (CNN), Support Vector Machines (SVM), and k-Nearest Neighbors (KNN), our model demonstrated an impressive 86% accuracy in identifying diabetic retinopathy from retinal images. Focusing on glaucoma detection, we utilized the RIGA dataset, comprising 2,664 images, which are categorized into non-glaucoma (1,488 images) and glaucoma (1,176 images). Our model showcased a commendable testing accuracy of 94%. This endeavor not only amplifies the potential of machine learning in glaucoma prediction but also signifies a step forward in creating a user-centric, efficient diagnostic tool. Such innovations are pivotal in enhancing global eye care and reducing the prevalence of vision impairment due to glaucoma.

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Section: Convolutional Neural Network For Glaucoma Detectionmentioning

confidence: 99%

Automated Glaucoma Detection in Retinal Fundus Images Using Machine Learning Models

Nagaratna P Hegde

2024

jes

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Progressive Collaborative Method for Protecting Users Privacy in Location-Based Services

Ramakrishna Reddy,

Sharma,

Anusha

et al. 2024

MATEC Web Conf.

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The development of new mobile communication and information service technologies has opened up exciting possibilities for location-based services. Users of location-based services (LBS) can access vital data from their service providers by utilizing their location data. Maps and navigation, information services, tourist information services, social networking, and many more popular applications are available. A user's location and other personal details must be submitted to the providers of location-based services in order for them to work. For example, details about one's whereabouts and identity. By "location privacy," we mean the idea that third parties shouldn't be able to track a user's precise whereabouts. It is important that users' sensitive information be hidden from unauthorized individuals when communicating. Most difficult in LBS location-based are concerns about communications and data. Each peer does their duty reciprocally in a collaborative method, which is a completely distributed technique. For the most secure and private location-based services (LBS), it employs cryptographic methods. The number of people using LBS is growing at a rapid pace these days. At this time, there isn't a single method available that has scalability capabilities. Building a realistic and computationally efficient solution that offers high privacy while decreasing processing overhead and improving scalability is a challenging task. The suggested method is cost-effective, supports scaling, is highly resilient against security and privacy assaults, and ensures privacy.

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Enhancing Mammal Conservation Through Automated Species Classification Using Deep Learning

Divya,

Rajesh,

Babu

et al. 2024

2024 First International Conference on Innovations in Communications, Electrical and Computer Engineering (ICICEC)

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Melanoma classification using convolutional neural network

Cited by 3 publications

References 18 publications

Automated Glaucoma Detection in Retinal Fundus Images Using Machine Learning Models

Automated Glaucoma Detection in Retinal Fundus Images Using Machine Learning Models

Progressive Collaborative Method for Protecting Users Privacy in Location-Based Services

Enhancing Mammal Conservation Through Automated Species Classification Using Deep Learning

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