Brain Tumor: Hybrid Feature Extraction Based on UNet and 3DCNN

Rajagopal, Sureshkumar; Thanarajan, Tamilvizhi; Alotaibi, Youseef; Alghamdi, Saleh

doi:10.32604/csse.2023.032488

Cited by 27 publications

(18 citation statements)

References 57 publications

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“…Individual data points are at the bottom of the dendrogram, while the largest clusters, which contain all of the data points, are at the top. The dendrogram can be cut at different heights to yield varying numbers of clusters [7]. Section 2 discusses the related work of the existing model in detecting and categorizing coffee aromas.…”

Section: Figure 1 Coffee Smell and Categoriesmentioning

confidence: 99%

Using Hierarchical Agglomerative Clustering in E-Nose for Coffee Aroma Profiling: Identification, Quantification, and Disease Detection

Selvanarayanan,

Rajandran,

Alotaibi

2023

I2M

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Numerous coffee devotees believe that the coffee smell plays a vital role in the coffeedrinking insight, complementing the taste and enhancing delight. In the traditional strategy, aroma patterns and profiles are observed by extensive investigation of human olfaction. However, the outcome tends to be imprecise. Tackling the difficulties encountered in distinct scent profiles linked to various coffee bean varieties, including Arabica, Robusta, Monsoon Malabar, Chikmagalur, and Coorg coffee, as well as diverse roasting techniques, through the utilization of Electronic Nose Applications for the investigation of coffee aromas. The suggested methodology employs e-nose technology utilizing conducting polymer sensors to detect aroma volatile chemicals found in coffee, including furaneol, 2-methylisoborneol, and 3-methylindole. The e-nose olfactory characteristics of coffee beans at various stages of roasting are systematically examined and discernible patterns are duly identified. The average intensity of the coffee aroma perceived at a distance of 10 centimeters was rated as 3.9 on a scale of 5. The observed standard deviation of coffee aroma intensity at a distance of 10 centimeters was determined to be 3.8 on a scale of 5. The p-value associated with the disparity in average fragrance scores was determined to be 0.05.

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Section: Figure 1 Coffee Smell and Categoriesmentioning

confidence: 99%

Using Hierarchical Agglomerative Clustering in E-Nose for Coffee Aroma Profiling: Identification, Quantification, and Disease Detection

Selvanarayanan,

Rajandran,

Alotaibi

2023

I2M

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“…• 2D U-Net Model for Segmentation: The 2D U-Net model is designed for image segmentation tasks, where it classifies pixels in a 2D input image into predefined categories [32]. It features an encoder-decoder architecture with skip connections.…”

Section: B Datasetmentioning

confidence: 99%

Brain Tumor Detection Using 3D-UNet Segmentation Features and Hybrid Machine Learning Model

Mallampati,

Ishaq,

Rustam

et al. 2023

IEEE Access

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Machine learning has significantly improved disease diagnosis, enhancing the efficiency and accuracy of the healthcare system. One critical area where it proves beneficial is diagnosing brain tumors, a life-threatening disease, where early and accurate predictions can save lives. This study focuses on deploying a machine learning-based approach for brain tumor detection, utilizing Magnetic Resonance Imaging (MRI) features. We train the proposed model using 3D-UNet and 2D-UNet segmentation features extracted from MRI, encompassing shape, statistics, gray level size zone matrix, gray level dependence matrix, gray level co-occurrence matrix, and gray level run length matrix values. To improve performance, we propose a hybrid model that combines the strengths of two machine learning models, K-nearest neighbor (KNN) and gradient boosting classifier (GBC), using soft voting criteria. We combine them because, in cases where KNN exhibits poor performance for certain data points, GBC demonstrates significant performance, and vice versa, where GBC shows poor results, KNN performs significantly better. With 2D-UNet segmentation features, the model achieves a 64% accuracy. By training it on 3D-UNet segmentation features, we achieve a significant accuracy of 71% which surpasses existing state-of-the-art models that utilize 3D-UNet segmentation features.

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“…Deep learning enables the direct use of raw data [6] without the need for the usage of manually produced features. The use of deep learning in computer vision has received significant attention in recent years, leading to the development of a number of new approaches in the field [7]. The CNN-based system proposed by Lu et al [8] can accurately identify 10 common rice diseases, including rice blast, rice false smut, rice sheath blight, foolish seedling disease, rice bacterial leaf blight, rice brown spot, rice seeding blight, rice sheath rot, rice bacterial sheath root, and rice bacterial wilt [9].…”

Section: Introductionmentioning

confidence: 99%

Plant Disease Classification Based on ConvLSTM U-Net with Fully Connected Convolutional Layers

Alharbi¹,

Rajagopal²,

Rajendran³

et al. 2023

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Plants are susceptible to a variety of illnesses throughout their growth stages. One of the trickiest issues in agriculture is the early diagnosis of plant diseases. The entire output may be negatively impacted by infections if they are not discovered early on, which would lower farmers' profitability. Numerous researchers have proposed numerous cutting-edge solutions based on Deep Learning and Machine Learning techniques to address this issue. However, the majority of these systems either has poor classification accuracy rates or utilizes millions of training parameters. In this research, a novel model using ConvLSTM U Net-based automatic detection of plant disease is proposed. To the best of our knowledge, no state-of-the-art systems described in the literature have a hybrid system based on CAE and CNN to automatically identify plant diseases. The proposed model employed in this study is to identify the presence of Bacterial Spot disease in medicinal plants using the image of their leaves, but it may be extended to identifying any plant disease. The work conducted for this research employ a dataset that is readily accessible to get images of medicinal plant leaves. In comparison to previous methods described in the literature, the proposed ConLSTM U-Net model requires for less training parameters. As a consequence of this, the amount of time necessary to train the model for automatic plant disease detection and the amount of time required to diagnose the disease in plants using the trained model are both significantly decreased.

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Brain Tumor: Hybrid Feature Extraction Based on UNet and 3DCNN

Cited by 27 publications

References 57 publications

Using Hierarchical Agglomerative Clustering in E-Nose for Coffee Aroma Profiling: Identification, Quantification, and Disease Detection

Using Hierarchical Agglomerative Clustering in E-Nose for Coffee Aroma Profiling: Identification, Quantification, and Disease Detection

Brain Tumor Detection Using 3D-UNet Segmentation Features and Hybrid Machine Learning Model

Plant Disease Classification Based on ConvLSTM U-Net with Fully Connected Convolutional Layers

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