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2 2022

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Plant disease detection using ensembled CNN framework

Mondal Subhash, Banerjee Suharta, Mukherjee Subinoy, Sengupta Diganta

Computer Science

2022

T. 23 (3)

321--333

Agriculture exhibits the prime driving force for the growth of agro-based economies globally. In agriculture, detecting and preventing crops from the attacks of pests is a primary concern in today’s world. The early detection of plant disease becomes necessary in order to avoid the degradation of the yield of crop production. In this paper, we propose an ensemble-based convolutional neural network (CNN) architecture that detects plant disease from the images of a plant’s leaves. The proposed architecture considers CNN architectures like VGG-19, ResNet-50, and InceptionV3 as its base models, and the prediction from these models is used as an input for our meta-model (Inception-ResNetV2). This approach helped us build a generalized model for disease detection with an accuracy of 97.9% under test conditions.

Ovarian cancer diagnosis using pretrained mask CNN-based segmentation with VGG-19 architecture

Senthil Kavitha, Vidyaathulasiraman

Bio-Algorithms and Med-Systems

2022

Vol. 18, no. 1

84--95

Objectives: This paper proposed the neural network-based segmentation model using Pre-trained Mask Convolutional Neural Network (CNN) with VGG-19 architecture. Since ovarian is very tiny tissue, it needs to be segmented with higher accuracy from the annotated image of ovary images collected in dataset. This model is proposed to predict and suppress the illness early and to correctly diagnose it, helping the doctor save the patient's life. Methods: The paper uses the neural network based segmentation using Pre-trained Mask CNN integrated with VGG-19 NN architecture for CNN to enhance the ovarian cancer prediction and diagnosis. Results: Proposed segmentation using hybrid neural network of CNN will provide higher accuracy when compared with logistic regression, Gaussian naïve Bayes, and random Forest and Support Vector Machine (SVM) classifiers.