MRI Scans for Deep Learning-Based Chronic Nephropathy Detection: A Comparison of CNN, MobileNet, VGG16, and ResNet-50 Models

Amanatulla, Md.; Swathi, G.; Pallavi, M.; Bindu, K.Pavana

doi:10.1109/incet61516.2024.10593144

2024 5th International Conference for Emerging Technology (INCET) 2024

DOI: 10.1109/incet61516.2024.10593144

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MRI Scans for Deep Learning-Based Chronic Nephropathy Detection: A Comparison of CNN, MobileNet, VGG16, and ResNet-50 Models

Md. Amanatulla,

G. Swathi,

M. Pallavi

et al.

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Disease diagnosis in Cassava leaves using CNN design and ResNet algorithm

Bandlamudi,

Polagani

et al. 2024

Preprint

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Growth rate of crops is significantly lowered by illnesses that affect plants. It is impossible for anyone to eat the crops since they are tainted with various diseases. Farmers may suffer enormous losses as a consequence. Since cassava is an important food source in several countries, the financial system might be seriously damaged by the issue at hand. Traditional plant pathogen detection is labour-intensive and error-prone. It is not typically a dependable strategy to identify and stop the spread of plant viruses. Innovative technologies like deep learning as well as machine learning might aid in the early detection of plant diseases as an approach to get around these problems. The main goal of the work is to employ deep learning to image classification in order to accurately identify diseases that especially impact cassava plants. This recognition may make it possible to implement preventative measures like the specific application of chemical pesticides or confinement of contaminated crops. Each and every training and testing image comes from a rural area in the natural world. Using a specific collection of information, the simulation has been verified to ascertain its true results. The installation of a precise disease identification and mitigation model has the potential to significantly increase the durability of the cassava crop, improving food production and the quality of life for millions of people who are dependent upon this valuable crop.

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Disease diagnosis in Cassava leaves using CNN design and ResNet algorithm

Bandlamudi,

Polagani

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

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MRI Scans for Deep Learning-Based Chronic Nephropathy Detection: A Comparison of CNN, MobileNet, VGG16, and ResNet-50 Models

Cited by 1 publication

References 40 publications

Disease diagnosis in Cassava leaves using CNN design and ResNet algorithm

Disease diagnosis in Cassava leaves using CNN design and ResNet algorithm

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