CCDF: Automatic system for segmentation and recognition of fruit crops diseases based on correlation coefficient and deep CNN features

Khan, Muhammad Attique; Akram, Tallha; Sharif, Muhammad; Awais, Muhammad; Javed, Kashif; Ali, Hashim; Saba, Tanzila

doi:10.1016/j.compag.2018.10.013

Cited by 190 publications

(95 citation statements)

References 18 publications

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“…In pattern recognition, various types of features are extracted for the classification process such as texture, shape, color, deep, and few more (T. Akram, Khan, Sharif, & Yasmin, ; M. A. Khan, Akram, Sharif, Awais, et al, ; Sharif, Tanvir, Munir, Khan, & Yasmin, ). As candidate nodules contain both nodules and non‐nodules so for the purpose of classification, we have extracted both texture and edge features.…”

Section: Methodsmentioning

confidence: 99%

“…In pattern recognition, various types of features are extracted for the classification process such as texture, shape, color, deep, and few M. A. Khan, Akram, Sharif, Awais, et al, 2018;Sharif, Tanvir, Munir, Khan, & Yasmin, 2018).…”

Section: Texture and Edge Feature Extractionmentioning

confidence: 99%

“…In the past few years, researchers have focused their attention on the development of automated tools and systems in the domain of computer vision that could detect and classify the anomalies in lesions in computed tomography (CT) and other imageries (Abbas et al, ; Abbas, Saba, Mohamad, et al, ; Abbas, Saba, Rehman, et al, ; M. A. Khan, Akram, Sharif, Awais, et al, ; M. A. Khan, Akram, Sharif, Javed, et al, ; M. A. Khan, Akram, Sharif, Shahzad, et al, ; Nasir et al, ; Rehman, Abbas, Saba, Mahmood, & Kolivand, ; Rehman, Abbas, Saba, Mehmood, et al, ; Rehman, Abbas, Saba, Rahman, et al, ; Saba et al, ; Yousaf et al, ). Majority of the previous research work has focused on the early detection of lungs cancer using the texture‐based interpretation of chest CTs (Reeves & Kostis, ).…”

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

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Lungs nodule detection framework from computed tomography images using support vector machine

Khan

Nazir

Khan

et al. 2019

Microscopy Res & Technique

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The emergence of cloud infrastructure has the potential to provide significant benefits in a variety of areas in the medical imaging field. The driving force behind the extensive use of cloud infrastructure for medical image processing is the exponential increase in the size of computed tomography (CT) and magnetic resonance imaging (MRI) data. The size of a single CT/MRI image has increased manifold since the inception of these imagery techniques. This demand for the introduction of effective and efficient frameworks for extracting relevant and most suitable information (features) from these sizeable images. As early detection of lungs cancer can significantly increase the chances of survival of a lung scanner patient, an effective and efficient nodule detection system can play a vital role. In this article, we have proposed a novel classification framework for lungs nodule classification with less false positive rates (FPRs), high accuracy, sensitivity rate, less computationally expensive and uses a small set of features while preserving edge and texture information. The proposed framework comprises multiple phases that include image contrast enhancement, segmentation, feature extraction, followed by an employment of these features for training and testing of a selected classifier. Image preprocessing and feature selection being the primary steps—playing their vital role in achieving improved classification accuracy. We have empirically tested the efficacy of our technique by utilizing the well‐known Lungs Image Consortium Database dataset. The results prove that the technique is highly effective for reducing FPRs with an impressive sensitivity rate of 97.45%.

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Section: Methodsmentioning

confidence: 99%

Section: Texture and Edge Feature Extractionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

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Lungs nodule detection framework from computed tomography images using support vector machine

Khan

Nazir

Khan

et al. 2019

Microscopy Res & Technique

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“…Brain tumors are also categorized based on their origin: primary brain tumors are the lesions that emerge within the brain whereas secondary brain tumors (metastatic tumors) originate at a different location of the body and move to the brain. There are different categorization schemes designed by researchers but the scheme designed by the World Health Organization (WHO) is considered to be the standard (Aurangzeb, Muhammad Attique, Saba, Javed, & Iqbal, ; Fahad, Khan, Saba, Rehman, & Iqbal, ; Husham, Alkawaz, Saba, Rehman, & Alghamdi, ; Jamal, Hazim Alkawaz, Rehman, & Saba, ; Khan et al, , ; Rehman, Abbas, Saba, Rahman, et al, ; Rehman, Abbas, Saba, Mehmood, et al, ; Rehman, Abbas, Saba, Mahmood, & Kolivand, ; Saba, ; Saba, Al‐Zahrani, & Rehman, ; Saba, Rehman, Mehmood, Kolivand, Uddin, et al, ; Saba, Rehman, Mehmood, Kolivand, & Sharif, ). WHO has made more than 120 categories for a brain tumor and each category is further graded from I to IV (Louis et al, ).…”

Section: Introductionmentioning

confidence: 99%

Deep learning model integrating features and novel classifiers fusion for brain tumor segmentation

Iqbal

Khan

Saba

et al. 2019

Microscopy Res & Technique

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Automatic and precise segmentation and classification of tumor area in medical images is still a challenging task in medical research. Most of the conventional neural network based models usefully connected or convolutional neural networks to perform segmentation and classification. In this research, we present deep learning models using long short term memory (LSTM) and convolutional neural networks (ConvNet) for accurate brain tumor delineation from benchmark medical images. The two different models, that is, ConvNet and LSTM networks are trained using the same data set and combined to form an ensemble to improve the results. We used publicly available MICCAI BRATS 2015 brain cancer data set consisting of MRI images of four modalities T1, T2, T1c, and FLAIR. To enhance the quality of input images, multiple combinations of preprocessing methods such as noise removal, histogram equalization, and edge enhancement are formulated and best performer combination is applied. To cope with the class imbalance problem, class weighting is used in proposed models. The trained models are tested on validation data set taken from the same image set and results obtained from each model are reported. The individual score (accuracy) of ConvNet is found 75% whereas for LSTM based network produced 80% and ensemble fusion produced 82.29% accuracy.

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Image Processing Technology, Imaging Techniques, and Their Application in the Food Processing Sector

Ghanghas,

Kumar,

Singh

et al. 2024

Nonthermal Food Engineering Operations

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CCDF: Automatic system for segmentation and recognition of fruit crops diseases based on correlation coefficient and deep CNN features

Cited by 190 publications

References 18 publications

Lungs nodule detection framework from computed tomography images using support vector machine

Lungs nodule detection framework from computed tomography images using support vector machine

Deep learning model integrating features and novel classifiers fusion for brain tumor segmentation

Image Processing Technology, Imaging Techniques, and Their Application in the Food Processing Sector

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