Megha Trivedi - Academia.edu (original) (raw)

Papers by Megha Trivedi

Research paper thumbnail of Automatic monitoring of the growth of plants using deep learning-based leaf segmentation

International Journal of Applied Science and Engineering, 2021

Plants are a source of food, medicines, fiber, fuel, etc. and are therefore crucial for our survi... more Plants are a source of food, medicines, fiber, fuel, etc. and are therefore crucial for our survival. Due to this, intensive care of plants should be done and it requires monitoring of their growth, size, yield, etc. However, manually monitoring such factors is often time-consuming and necessitates one to have in-depth knowledge of agriculture and plants. Thus, automatic systems for plant image analysis would be beneficial for practical and productive agriculture. Therefore, an automatic method is proposed for monitoring the growth of plants by first performing the segmentation of leaves in plant images and then calculating the segmented area. A deep learning-based architecture “U-Net” was used for the segmentation task. A benchmark dataset of 810 images was used to train and test the proposed deep learning network. The proposed model was trained within 3 hours and achieved a dice accuracy of 94.91% on the training set, 94.93% on the validation set, and 95.05% on the testing set. Th...

Research paper thumbnail of A lightweight deep learning architecture for the automatic detection of pneumonia using chest X-ray images

Multimedia Tools and Applications, 2021

Pneumonia is a life-threatening respiratory lung disease. Children are more prone to be affected ... more Pneumonia is a life-threatening respiratory lung disease. Children are more prone to be affected by the disease and accurate manual detection is not easy. Generally, chest radiographs are used for the manual detection of pneumonia and expert radiologists are required for the assessment of the X-ray images. An automatic system would be beneficial for the diagnosis of pneumonia based on chest radiographs as manual detection is time-consuming and tedious. Therefore, a method is proposed in this paper for the fast and automatic detection of pneumonia. A deep learning-based architecture 'MobileNet' is proposed for the automatic detection of pneumonia based on the chest X-ray images. A benchmark dataset of 5856 chest X-ray images was taken for the training, testing, and evaluation of the proposed deep learning network. The proposed model was trained within 3 Hrs. and achieved a training accuracy of 97.34%, a validation accuracy of 87.5%, and a testing accuracy of 94.23% for automatic detection of pneumonia. However, the combined accuracy was achieved as 97.09% with 0.96 specificity, 0.97 precision, 0.98 recall, and 0.97 F-Score. The proposed method was found faster and computationally lesser expensive as compared to other methods in the literature and achieved a promising accuracy.

Research paper thumbnail of Automatic monitoring of the growth of plants using deep learning-based leaf segmentation

International Journal of Applied Science and Engineering, 2021

Plants are a source of food, medicines, fiber, fuel, etc. and are therefore crucial for our survi... more Plants are a source of food, medicines, fiber, fuel, etc. and are therefore crucial for our survival. Due to this, intensive care of plants should be done and it requires monitoring of their growth, size, yield, etc. However, manually monitoring such factors is often time-consuming and necessitates one to have in-depth knowledge of agriculture and plants. Thus, automatic systems for plant image analysis would be beneficial for practical and productive agriculture. Therefore, an automatic method is proposed for monitoring the growth of plants by first performing the segmentation of leaves in plant images and then calculating the segmented area. A deep learning-based architecture “U-Net” was used for the segmentation task. A benchmark dataset of 810 images was used to train and test the proposed deep learning network. The proposed model was trained within 3 hours and achieved a dice accuracy of 94.91% on the training set, 94.93% on the validation set, and 95.05% on the testing set. Th...

Research paper thumbnail of A lightweight deep learning architecture for the automatic detection of pneumonia using chest X-ray images

Multimedia Tools and Applications, 2021

Pneumonia is a life-threatening respiratory lung disease. Children are more prone to be affected ... more Pneumonia is a life-threatening respiratory lung disease. Children are more prone to be affected by the disease and accurate manual detection is not easy. Generally, chest radiographs are used for the manual detection of pneumonia and expert radiologists are required for the assessment of the X-ray images. An automatic system would be beneficial for the diagnosis of pneumonia based on chest radiographs as manual detection is time-consuming and tedious. Therefore, a method is proposed in this paper for the fast and automatic detection of pneumonia. A deep learning-based architecture 'MobileNet' is proposed for the automatic detection of pneumonia based on the chest X-ray images. A benchmark dataset of 5856 chest X-ray images was taken for the training, testing, and evaluation of the proposed deep learning network. The proposed model was trained within 3 Hrs. and achieved a training accuracy of 97.34%, a validation accuracy of 87.5%, and a testing accuracy of 94.23% for automatic detection of pneumonia. However, the combined accuracy was achieved as 97.09% with 0.96 specificity, 0.97 precision, 0.98 recall, and 0.97 F-Score. The proposed method was found faster and computationally lesser expensive as compared to other methods in the literature and achieved a promising accuracy.