A Deep learning Framework for Eye Melanoma Detection employing Convolutional Neural Network

Ganguly, Biswarup; Biswas, Shreya; Ghosh, Samrat; Maiti, Sabyasachi; Bodhak, Snehasish

doi:10.1109/iccece44727.2019.9001858

Cited by 29 publications

(6 citation statements)

References 6 publications

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“…The experimental results demonstrate a precision of 100% for MESSIDOR and 98% for the Magrabi datasets. Biswarup Ganguly et al [2] proposed a melanoma detection system. The used dataset consists of 170 images, with 60% for training and 40% for assessment.…”

Section: IImentioning

confidence: 99%

“…2) Convolution Layers: Image input through a 2D convolution layer process that is executed in three phases with 16, 32, and 64 filters that are alternately executed with MaxPooling. This layer is used to derive salient features and information from images displayed on each screen [2].…”

Section: Fig 3 Cnn Architecturementioning

confidence: 99%

“…Deep Learning's algorithm is able to classify classes based on the input that has been provided. Deep learning networks can accelerate classification with extremely accurate results [2]. Eye disease is one of the most common diseases of the modern era [3].…”

Section: Introductionmentioning

confidence: 99%

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Early Detection of Eye Disease Using CNN

Erdin¹,

Patel²

2023

IJRASET

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The eyes perform an essential part of life. The eye is one of the organs that assist humans learn about their natural environments and collect information from them. Almost everywhere in the world, the frequency of eye disease has increased, needing a serious response. Immediate eye detection will be invaluable assistance in offering further treatment to prevent blindness. In this study, a Convolution Neural Network model is used for identifying eye diseases. This research aims to categorize human eyes into four categories: trachoma, conjunctivitis, cataract, and healthy. The investigation got an accuracy of 88.36%, while the CNN model evaluation provided Precision of 89.25%, Recall of 88.75%, and F1 Score of 88.5%. Based on the accuracy and evaluation results, this system can be used for the early detection of multiple eye diseases. Several random samples were also used for testing in this investigation. The test results indicate that this system is functional.

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Section: IImentioning

confidence: 99%

Section: Fig 3 Cnn Architecturementioning

confidence: 99%

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Early Detection of Eye Disease Using CNN

Erdin¹,

Patel²

2023

IJRASET

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“…There are many studies on the classification of Eye tumors. Biswarup et al [14] presented a system for classifying eye melanoma in medical images. The system is divided into three steps; images preprocessing step, images marked as tumors or not by medical experts and classification.…”

Section: Related Workmentioning

confidence: 99%

Detection of Orbital Tumors on MRI images using Convolutional Neural Networks

Allam,

Alfonse,

Salem

2023

IJICIS

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Orbital tumors are the most common type of tumor affecting the orbit. Some factors, such as technical causes relating to imaging quality and human error, contribute to radiologists misdiagnosing eye tumors. Computer-aided detection systems (CADs) are being developed to address these limitations and have recently been used in numerous imaging modalities for eye tumor diagnosis. CAD technologies increase radiologists' ability to detect and distinguish between normal and diseased tissues. These techniques are only conducted as a second opinion, but the radiologist makes the final decisions. This article presents the contemporary CAD method for detecting orbital tumors on magnetic resonance imaging (MRI) utilizing Convolutional Neural Networks (CNN). Pre-processing, Data Augmentation, Classification, and Evaluation are the four stages that involve our CAD system. Two datasets were used for MRI images: 1404 MRI T1-weighted images and 1560 MRI T2-weighted images. The system was evaluated by many evaluation metrics including the recognition rate which gives 95% for T1-weighted images and 94% for T2-weighted images.

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“…where, ( 9) is the transfer function of a 5-point derivative filter with a gain of 0.1 and processing delay of 16 samples. The filtered signal is squared as shown in (10) to enhance the peaks for proper detection as:…”

Section: B Arrythmia Detection Using Svmmentioning

confidence: 99%

Identification and Classification of Human Mental Stress using Physiological Data: A Low-Power Hybrid Approach

Roy

Acharya

Biswas

et al. 2022

2022 IEEE 6th International Conference on Condition Assessment Techniques in Electrical Systems (CATCON)

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This paper proposes a framework using artificial intelligence for recognizing human mental stress based on physiological data. It comprises three stages; at first, emotion detection has been carried out from the facial image using a deep learning-based framework employing convolutional neural networks (CNN). Secondly, electrocardiogram (ECG) signals have been acquired with the help of the developed sensor-based system integrating Arduino UNO with AD8232 sensor. Finally, preprocessing has been performed and extracted features from the ECG signals are fed to support vector machines (SVM) for identification of arrhythmia and hence diagnosis of mental stress. All three aspects are connected together via the overall output calculation. A score is added in accordance to the output for each of these three stages. The range of the final score (i.e., the overall output) decides the level of mental stress suffered by the person. The proposed method has been trained on FER-2013 for emotion detection and on MIT-BIH for arrhythmia detection. It is found that the performance outperforms other machine learning and deep learning models without much compromise in accuracy. The proposed method can perform classification based on the patient's current condition and gives an insight of the degree and time duration of stress of the patient.

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A Deep learning Framework for Eye Melanoma Detection employing Convolutional Neural Network

Cited by 29 publications

References 6 publications

Early Detection of Eye Disease Using CNN

Early Detection of Eye Disease Using CNN

Detection of Orbital Tumors on MRI images using Convolutional Neural Networks

Identification and Classification of Human Mental Stress using Physiological Data: A Low-Power Hybrid Approach

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