An improved faster-RCNN model for handwritten character recognition

Albahli, Saleh; Nawaz, Marriam; Javed, Ali; Irtaza, Aun

doi:10.1007/s13369-021-05471-4

Cited by 71 publications

(36 citation statements)

References 68 publications

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“…The proposed feature computer DenseNet-77 contains a small number of parameters with thinner layers network as compare to ResNet-101 which gives a computational benefit over ResNet-101. DenseNet has numerous dense blocks (DBs) that are consecutively linked with each other via employing added convolutional and pooling layers in sequential dense blocks [51,52]. DenseNet model can show the complicated transformation efficiently which assists in dealing with the problem of the lack of the object's location data for the significant features to some extent.…”

Section: Custom Centernetmentioning

confidence: 99%

A novel deep learning method for detection and classification of plant diseases

Albattah

Nawaz

Javed

et al. 2021

Complex Intell. Syst.

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138

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The agricultural production rate plays a pivotal role in the economic development of a country. However, plant diseases are the most significant impediment to the production and quality of food. The identification of plant diseases at an early stage is crucial for global health and wellbeing. The traditional diagnosis process involves visual assessment of an individual plant by a pathologist through on-site visits. However, manual examination for crop diseases is restricted because of less accuracy and the small accessibility of human resources. To tackle such issues, there is a demand to design automated approaches capable of efficiently detecting and categorizing numerous plant diseases. Precise identification and classification of plant diseases is a tedious job due because of the occurrence of low-intensity information in the image background and foreground, the huge color resemblance in the healthy and diseased plant areas, the occurrence of noise in the samples, and changes in the position, chrominance, structure, and size of plant leaves. To tackle the above-mentioned problems, we have introduced a robust plant disease classification system by introducing a Custom CenterNet framework with DenseNet-77 as a base network. The presented method follows three steps. In the first step, annotations are developed to get the region of interest. Secondly, an improved CenterNet is introduced in which DenseNet-77 is proposed for deep keypoints extraction. Finally, the one-stage detector CenterNet is used to detect and categorize several plant diseases. To conduct the performance analysis, we have used the PlantVillage Kaggle database, which is the standard dataset for plant diseases and challenges in terms of intensity variations, color changes, and differences found in the shapes and sizes of leaves. Both the qualitative and quantitative analysis confirms that the presented method is more proficient and reliable to identify and classify plant diseases than other latest approaches.

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Section: Custom Centernetmentioning

confidence: 99%

A novel deep learning method for detection and classification of plant diseases

Albattah

Nawaz

Javed

et al. 2021

Complex Intell. Syst.

Self Cite

138

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“…The drawback of this is that it is computationally complicated, i.e., it necessitates a vast number of parameters (187M parameters) and memory resources that unsurprisingly lead to low speed. To decrease the complexity, we adopt the DenseNet-41 [61,62] as the feature extractor to improve the extraction of features. The DenseNet-41 involves four densely correlated units with 41 layers and contains fewer parameters which provides it with a computational improvement than the Hourglass104.…”

Section: Feature Extraction Using Customized Backbone Networkmentioning

confidence: 99%

Analysis of Brain MRI Images Using Improved CornerNet Approach

et al. 2021

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The brain tumor is a deadly disease that is caused by the abnormal growth of brain cells, which affects the human blood cells and nerves. Timely and precise detection of brain tumors is an important task to avoid complex and painful treatment procedures, as it can assist doctors in surgical planning. Manual brain tumor detection is a time-consuming activity and highly dependent on the availability of area experts. Therefore, it is a need of the hour to design accurate automated systems for the detection and classification of various types of brain tumors. However, the exact localization and categorization of brain tumors is a challenging job due to extensive variations in their size, position, and structure. To deal with the challenges, we have presented a novel approach, namely, DenseNet-41-based CornerNet framework. The proposed solution comprises three steps. Initially, we develop annotations to locate the exact region of interest. In the second step, a custom CornerNet with DenseNet-41 as a base network is introduced to extract the deep features from the suspected samples. In the last step, the one-stage detector CornerNet is employed to locate and classify several brain tumors. To evaluate the proposed method, we have utilized two databases, namely, the Figshare and Brain MRI datasets, and attained an average accuracy of 98.8% and 98.5%, respectively. Both qualitative and quantitative analysis show that our approach is more proficient and consistent with detecting and classifying various types of brain tumors than other latest techniques.

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“…Transition layer 2 16 16 150 The extensive dense links increase FMs; therefore, the transition layer is introduced to reduce the key-points dimension from the previous DB, as discussed in [32,33]. The computed features from DenseNet-100 are down-sampled with the stride rate R = 4 and are then passed to compute three types of heads, described in the following sections.…”

Section: Feature Extraction Using Densenet-100mentioning

confidence: 99%

Detection of Diabetic Eye Disease from Retinal Images Using a Deep Learning Based CenterNet Model

Nazir

Nawaz

Rashid

et al. 2021

Sensors

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Diabetic retinopathy (DR) is an eye disease that alters the blood vessels of a person suffering from diabetes. Diabetic macular edema (DME) occurs when DR affects the macula, which causes fluid accumulation in the macula. Efficient screening systems require experts to manually analyze images to recognize diseases. However, due to the challenging nature of the screening method and lack of trained human resources, devising effective screening-oriented treatment is an expensive task. Automated systems are trying to cope with these challenges; however, these methods do not generalize well to multiple diseases and real-world scenarios. To solve the aforementioned issues, we propose a new method comprising two main steps. The first involves dataset preparation and feature extraction and the other relates to improving a custom deep learning based CenterNet model trained for eye disease classification. Initially, we generate annotations for suspected samples to locate the precise region of interest, while the other part of the proposed solution trains the Center Net model over annotated images. Specifically, we use DenseNet-100 as a feature extraction method on which the one-stage detector, CenterNet, is employed to localize and classify the disease lesions. We evaluated our method over challenging datasets, namely, APTOS-2019 and IDRiD, and attained average accuracy of 97.93% and 98.10%, respectively. We also performed cross-dataset validation with benchmark EYEPACS and Diaretdb1 datasets. Both qualitative and quantitative results demonstrate that our proposed approach outperforms state-of-the-art methods due to more effective localization power of CenterNet, as it can easily recognize small lesions and deal with over-fitted training data. Our proposed framework is proficient in correctly locating and classifying disease lesions. In comparison to existing DR and DME classification approaches, our method can extract representative key points from low-intensity and noisy images and accurately classify them. Hence our approach can play an important role in automated detection and recognition of DR and DME lesions.

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An improved faster-RCNN model for handwritten character recognition

Cited by 71 publications

References 68 publications

A novel deep learning method for detection and classification of plant diseases

A novel deep learning method for detection and classification of plant diseases

Analysis of Brain MRI Images Using Improved CornerNet Approach

Detection of Diabetic Eye Disease from Retinal Images Using a Deep Learning Based CenterNet Model

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