Techniques of Deep Learning for Image Recognition

Patil, Ganesh G.; Banyal, Rohitash Kumar

doi:10.1109/i2ct45611.2019.9033628

Cited by 9 publications

(4 citation statements)

References 15 publications

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“…CNN follows controlled learning that utilises text, sound, and image data and can be used to detect the intersection of malaria and malaria illness because deep learning has the Detection of Female Anopheles Mosquito-Infected Cells: Exploring CNN, ReLU, and Sigmoid Activation Methods 3 capacity to acquire beneficial characteristics and does not require any manual extraction [11]. The majority of object recognition algorithms used today have focused on a select few widely used purposes, such as faces, cars, and people.…”

Section: Related Workmentioning

confidence: 99%

Detection of Female Anopheles Mosquito-Infected Cells: Exploring CNN, ReLU, and Sigmoid Activation Methods

Leena Jenifer,

Indumathi,

Mahalakshmi

2024

EAI Endorsed Trans Perv Health Tech

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INTRODUCTION: Deep learning uses multi-layer neural networks where the algorithm decides for itself whether aspects are essential for analysis based on the raw input. In general, deep learning networks get better as more data is used to train them. For a variety of applications, convolutional neural networks are frequently used to analyse, categorize, and detect images. OBJECTIVES: The proposed system technique is used for automated analysis of malaria-detecting frameworks. A female Anopheles mosquito bite is the primary method of transmission of the blood disease malaria. It is still common to manually count and identify parasitized cells during microscopic examination of either thick or thin layers of haemoglobin, which takes time for disease prognosis. METHODS: The current research uses a neural network based on convolution to catalogue images of cells with and without malaria infection. This method improves the precision of classification for the datasets under study. The ReLu activation function used by this model enables it to learn more quickly and perform more effectively. RESULTS: The prediction of infected and healthy cells was done accurately by the proposed model, which uses only 3 layers of convolution, and this was the idea behind the implementation. The model achieved an improved accuracy of 99.77% across 12 iterations (epochs). CONCLUSION: The proposed model is straightforward and successful in differentiating between malaria-infected and uninfected cells.

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Section: Related Workmentioning

confidence: 99%

Detection of Female Anopheles Mosquito-Infected Cells: Exploring CNN, ReLU, and Sigmoid Activation Methods

Leena Jenifer,

Indumathi,

Mahalakshmi

2024

EAI Endorsed Trans Perv Health Tech

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“…Deep learning methods for action recognition based on the video: with the tremendous success of deep learning methods on image recognition [23], object detection [24] and image segment [25] tasks, in recent years, a series of methods have been developed to learn deep-layer feature information by convolutional neural networks (CNNs) for video-based action recognition. The performance of deep learning methods outperforms traditional handcrafted methods.…”

Section: Related Workmentioning

confidence: 99%

Hi-EADN: Hierarchical Excitation Aggregation and Disentanglement Frameworks for Action Recognition Based on Videos

Lee

2021

Symmetry

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Most existing video action recognition methods mainly rely on high-level semantic information from convolutional neural networks (CNNs) but ignore the discrepancies of different information streams. However, it does not normally consider both long-distance aggregations and short-range motions. Thus, to solve these problems, we propose hierarchical excitation aggregation and disentanglement networks (Hi-EADNs), which include multiple frame excitation aggregation (MFEA) and a feature squeeze-and-excitation hierarchical disentanglement (SEHD) module. MFEA specifically uses long-short range motion modelling and calculates the feature-level temporal difference. The SEHD module utilizes these differences to optimize the weights of each spatiotemporal feature and excite motion-sensitive channels. Moreover, without introducing additional parameters, this feature information is processed with a series of squeezes and excitations, and multiple temporal aggregations with neighbourhoods can enhance the interaction of different motion frames. Extensive experimental results confirm our proposed Hi-EADN method effectiveness on the UCF101 and HMDB51 benchmark datasets, where the top-5 accuracy is 93.5% and 76.96%.

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“…The research proposed by Patil and Banyal [9] entitled "Techniques of deep learning for image recognition" has studied the various approaches and techniques available for deep learning in image recognition. The various learning modes such as supervised, unsupervised with various data inputs have yielded results where CNN has been proved as the optimal technique for image recognition.…”

Section: Introductionmentioning

confidence: 99%

A disaster classification application using convolutional neural network by performing data augmentation

Sobhana¹,

Ch²,

Indira³

et al. 2022

IJEECS

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Natural disasters are catastrophic events and cause havoc to human life. These events occur in the most unpredictable times and are beyond human control. The aftermath of the disasters is devastating ranging from loss of life to relocation of large groups of the population. With the development in the domains of computer vision (CV) and Image processing, machine learning and deep learning models can integrate images and perform predictions. Deep learning techniques employ many robust techniques and provide significant results even in the case of images. The detection of natural disasters without human intervention requires the help of deep learning techniques. The project aims to employ a multi-layered convolutional neural network (CNN) organization to classify the images related to natural disasters related to earthquakes, floods, cyclones, and wildfires.

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Techniques of Deep Learning for Image Recognition

Cited by 9 publications

References 15 publications

Detection of Female Anopheles Mosquito-Infected Cells: Exploring CNN, ReLU, and Sigmoid Activation Methods

Detection of Female Anopheles Mosquito-Infected Cells: Exploring CNN, ReLU, and Sigmoid Activation Methods

Hi-EADN: Hierarchical Excitation Aggregation and Disentanglement Frameworks for Action Recognition Based on Videos

A disaster classification application using convolutional neural network by performing data augmentation

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