Skin Disease Detection Using Artificial Neural Network

Reddy, V. Jaychandra; Nagalakshmi, T.J.

doi:10.5958/0976-5506.2019.04183.4

Cited by 8 publications

(2 citation statements)

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“…This system has two parts first feature extraction, that was done by the image acquisition process. Second part was classification, which was done by the feed-forward neural network [5].…”

Section: Literature Reviewmentioning

confidence: 99%

Skin Disease Detection: Machine Learning vs Deep Learning

Bandyopadhyay¹,

Bhaumik²,

Poddar³

2021

Preprint

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Skin disease is a very common disease for humans. In the medical industry detecting skin disease and recognizing its type is a very challenging task. Due to the complexity of human skin texture and the visual closeness effect of the diseases, sometimes it is really difficult to detect the exact type. Therefore, it is necessary to detect and recognize the skin disease at its very first observation. In today's era, artificial intelligence (AI) is rapidly growing in medical fields. Different machine learning (ML) and deep learning(DL) algorithms are used for diagnostic purposes. These methods drastically improve the diagnosis process and also speed up the process. In this paper, a brief comparison between the machine learning process and the deep learning process was discussed. In both processes, three different and popular algorithms are used. For the machine Learning process Bagged Tree Ensemble, K-Nearest Neighbor (KNN), and Support Vector Machine(SVM) algorithms were used. For the deep learning process three pre-trained deep neural network models

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“…This system has two parts first feature extraction, that was done by the image acquisition process. Second part was classification, which was done by the feed-forward neural network [5].…”

Section: Literature Reviewmentioning

confidence: 99%

Skin Disease Detection: Machine Learning vs Deep Learning

Bandyopadhyay¹,

Bhaumik²,

Poddar³

2021

Preprint

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“…It is suggested to use a model that combines stacked auto encoder (SAE), bidirectional LSTM, and wavelet change. These frameworks provide superior findings when comparing the mean absolute error (MAE) and root mean square error (RMSE) of the experiment [9]- [12].…”

Section: Introductionmentioning

confidence: 99%

Deep learning and machine learning classification technique for integrated forecasting

Prem Monickaraj,

Rani Devakadacham,

Shanmugam

et al. 2024

IJ-AI

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Smart fisheries are increasingly using artificial intelligence (AI) technologies to increase their sustainability. The potential fishing zone (PFZ) forecasts several fish aggregation zones throughout the duration of the prediction in any sea. The autoregressive integrated moving average (ARIMA) and random forest model are used in the current study to provide a technique for locating viable fishing zones in deep marine seas. A significant amount of data was gathered for the database's creation, including monitoring information for Indian fishing fleets from 2017 to 2019. Using expert label datasets for validation, it was discovered that the model's detection accuracy was 98%. Our method uses salinity and dissolved oxygen, two crucial markers of water quality, to identify suitable fishing zones for the first time. In the current research, a system was created to identify and map the quantity of fishing activity. The tests use a number of parameter measurements to evaluate the contrast-enhanced computed tomography (CECT) approach to machine learning (ML) and deep learning (DL) methodologies. The findings showed that the CECT had a 94% accuracy rate compared to a convolutional neural network's 92% accuracy rate for the 80% training data and 20% testing data.

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