A Review on Diversified Mechanisms for Multi Focus Image Fusion

Dulhare, Uma N.; Khaled, Areej Mohammed; Ali, Mohd Hasham

doi:10.2139/ssrn.3424480

Cited by 6 publications

(4 citation statements)

References 8 publications

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“…execution of multiple algorithms of clustering and classification on different environments. Dulhare et al in [31] have presented a proposal to highlight the purpose of capturing improper images and opting for image fusion to overcome these defects. Dulhare et al in [32] have given a proposal about the integration of Taj Shenwi images using a series of partially focused images from the same scene.…”

Section: Case Study Thailandmentioning

confidence: 99%

“…Even on generating a solution for a challenge, there is still a possibility of high rising scrutiny in the advancements and developments of face detections [13]. Selecting a high-quality camera with a good aspect ratio [31], event analysis, and video surveillance are also considered as challenges. Several reasons were found for the poor achievement of existing face mask detection models as compared to the normal ones.…”

Section: Issues and Challenges In Existing Systemmentioning

confidence: 99%

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Automatic Real-Time Medical Mask Detection Using Deep Learning to Fight COVID-19

Rahmani¹,

Taranum²,

Nikhat³

et al. 2022

Computer Systems Science and Engineering

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The COVID-19 pandemic is a virus that has disastrous effects on human lives globally; still spreading like wildfire causing huge losses to humanity and economies. There is a need to follow few constraints like social distancing norms, personal hygiene, and masking up to effectively control the virus spread. The proposal is to detect the face frame and confirm the faces are properly covered with masks. By applying the concepts of Deep learning, the results obtained for mask detection are found to be effective. The system is trained using 4500 images to accurately judge and justify its accuracy. The aim is to develop an algorithm to automatically detect a mask, but the approach does not facilitate the percentage of improper usage. Accuracy levels are as low as 50% if the mask is improperly covered and an alert is raised for improper placement. It can be used at traffic places and social gatherings for the prevention of virus transmission. It works by first locating the region of interest by creating a frame boundary, then facial points are picked up to detect and concentrate on specific features. The training on the input images is performed using different epochs until the artificial face mask detection dataset is created. The system is implemented using Tensor-Flow with OpenCV and Python using a Jupyter Notebook simulation environment. The training dataset used is collected from a set of diverse open-source datasets with filtered images available at Kaggle Medical Mask Dataset by Mikolaj Witkowski, Kera, and Prajna Bhandary. To simulate MobilNetV2 classifier is used to load and pre-process the image dataset for building a fully connected head. The objective is to assess the accuracy of the identification, measuring the efficiency and effectiveness of algorithms for precision, recall, and F1 score.

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Section: Case Study Thailandmentioning

confidence: 99%

Section: Issues and Challenges In Existing Systemmentioning

confidence: 99%

Automatic Real-Time Medical Mask Detection Using Deep Learning to Fight COVID-19

Rahmani¹,

Taranum²,

Nikhat³

et al. 2022

Computer Systems Science and Engineering

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“…In early development on image fusion, spatial domain or transform domain methods were used commonly [ 21 , 22 , 23 , 24 ]. The spatial domain method is to process pixels directly.…”

Section: Introductionmentioning

confidence: 99%

“…Dulhare [ 24 ] reviewed and compared the image fusion methods in the spatial domain, transform domain and deep learning. The review includes several experiments, which the result has proved that the image fusion methods that’s based on deep learning are superior to the methods of spatial and transform domain.…”

Section: Introductionmentioning

confidence: 99%

Multi-Focus Image Fusion Based on Convolution Neural Network for Parkinson’s Disease Image Classification

et al. 2021

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Parkinson’s disease (PD) is a common neurodegenerative disease that has a significant impact on people’s lives. Early diagnosis is imperative since proper treatment stops the disease’s progression. With the rapid development of CAD techniques, there have been numerous applications of computer-aided diagnostic (CAD) techniques in the diagnosis of PD. In recent years, image fusion has been applied in various fields and is valuable in medical diagnosis. This paper mainly adopts a multi-focus image fusion method primarily based on deep convolutional neural networks to fuse magnetic resonance images (MRI) and positron emission tomography (PET) neural photographs into multi-modal images. Additionally, the study selected Alexnet, Densenet, ResNeSt, and Efficientnet neural networks to classify the single-modal MRI dataset and the multi-modal dataset. The test accuracy rates of the single-modal MRI dataset are 83.31%, 87.76%, 86.37%, and 86.44% on the Alexnet, Densenet, ResNeSt, and Efficientnet, respectively. Moreover, the test accuracy rates of the multi-modal fusion dataset on the Alexnet, Densenet, ResNeSt, and Efficientnet are 90.52%, 97.19%, 94.15%, and 93.39%. As per all four networks discussed above, it can be concluded that the test results for the multi-modal dataset are better than those for the single-modal MRI dataset. The experimental results showed that the multi-focus image fusion method according to deep learning can enhance the accuracy of PD image classification.

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Automation of Rice Leaf Diseases Prediction Using Deep Learning Hybrid Model VVIR

Gouse¹,

Dulhare²

2022

Advancements in Smart Computing and Information Security

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A Review on Diversified Mechanisms for Multi Focus Image Fusion

Cited by 6 publications

References 8 publications

Automatic Real-Time Medical Mask Detection Using Deep Learning to Fight COVID-19

Automatic Real-Time Medical Mask Detection Using Deep Learning to Fight COVID-19

Multi-Focus Image Fusion Based on Convolution Neural Network for Parkinson’s Disease Image Classification

Automation of Rice Leaf Diseases Prediction Using Deep Learning Hybrid Model VVIR

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