Detection and Classification of Gastrointestinal Diseases using Machine Learning

Naz, Javeria; Sharif, Muhammad; Yasmin, Mussarat; Raza, Mudassar; Khan, Muhammad Attique

doi:10.2174/1573405616666200928144626

Cited by 19 publications

(9 citation statements)

References 86 publications

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“…In another study, a set of features was extracted by the hybrid method and classified using CNN to detect digestive diseases in the image of MRI [35]. In a related study, a rapid feature extraction method using CNN technique was presented to detect inflammatory gastrointestinal diseases in WCT videos, and the extracted features were classified using SVM [36]. …”

Section: Related Workmentioning

confidence: 99%

Secure medical image transmission using deep neural network in e‐health applications

Alarood

Faheem

Al‐Khasawneh

et al. 2023

Healthcare Tech Letters

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Recently, medical technologies have developed, and the diagnosis of diseases through medical images has become very important. Medical images often pass through the branches of the network from one end to the other. Hence, high-level security is required. Problems arise due to unauthorized use of data in the image. One of the methods used to secure data in the image is encryption, which is one of the most effective techniques in this field. Confusion and diffusion are the two main steps addressed here. The contribution here is the adaptation of the deep neural network by the weight that has the highest impact on the output, whether it is an intermediate output or a semi-final output in additional to a chaotic system that is not detectable using deep neural network algorithm. The colour and grayscale images were used in the proposed method by dividing the images according to the Region of Interest by the deep neural network algorithm. The algorithm was then used to generate random numbers to randomly create a chaotic system based on the replacement of columns and rows, and randomly distribute the pixels on the designated area. The proposed algorithm evaluated in several ways, and compared with the existing methods to prove the worth of the proposed method.

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Section: Related Workmentioning

confidence: 99%

Secure medical image transmission using deep neural network in e‐health applications

Alarood

Faheem

Al‐Khasawneh

et al. 2023

Healthcare Tech Letters

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“…Classification refers to the process during which an input image is assigned a discrete class based on its features [51][52][53][54][55]. This class is the one with the highest probability score amongst all classes.…”

Section: Literature Reviewmentioning

confidence: 99%

Categorizing white blood cells by utilizing deep features of proposed 4B-AdditionNet-based CNN network with ant colony optimization

Raza

Shah

Sharif³

et al. 2021

Complex Intell. Syst.

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White blood cells, WBCs for short, are an essential component of the human immune system. These cells are our body's first line of defense against infections and diseases caused by bacteria, viruses, and fungi, as well as abnormal and external substances that may enter the bloodstream. A wrong WBC count can signify dangerous viral infections, autoimmune disorders, cancer, sarcoidosis, aplastic anemia, leukemia, tuberculosis, etc. A lot of these diseases and disorders can be extremely painful and often result in death. Leukemia is among the more common types of blood cancer and when left undetected leads to death. An early diagnosis is necessary which is possible by looking at the shapes and determining the numbers of young and immature WBCs to see if they are normal or not. Performing this task manually is a cumbersome, expensive, and time-consuming process for hematologists, and therefore computer-aided systems have been developed to help with this problem. This paper proposes an improved method of classification of WBCs utilizing a combination of preprocessing, convolutional neural networks (CNNs), feature selection algorithms, and classifiers. In preprocessing, contrast-limited adaptive histogram equalization (CLAHE) is applied to the input images. A CNN is designed and trained to be used for feature extraction along with ResNet50 and EfficientNetB0 networks. Ant colony optimization is used to select the best features which are then serially fused and passed onto classifiers such as support vector machine (SVM) and quadratic discriminant analysis (QDA) for classification. The classification accuracy achieved on the Blood Cell Images dataset is 98.44%, which shows the robustness of the proposed work.

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“…According to recent research, several deep learning methods and computer vision have improved GI endoscopic image diagnosis ( Haile et al, 2022 ; Naz et al, 2021 ). Despite the inclusion of a CNN module, many studies still necessitated complex procedures and relied on meticulous manual feature extraction techniques.…”

Section: Introductionmentioning

confidence: 99%

Development of a multi-fusion convolutional neural network (MF-CNN) for enhanced gastrointestinal disease diagnosis in endoscopy image analysis

Hossain,

Shamrat,

Zhou

et al. 2024

PeerJ Computer Science

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Gastrointestinal (GI) diseases are prevalent medical conditions that require accurate and timely diagnosis for effective treatment. To address this, we developed the Multi-Fusion Convolutional Neural Network (MF-CNN), a deep learning framework that strategically integrates and adapts elements from six deep learning models, enhancing feature extraction and classification of GI diseases from endoscopic images. The MF-CNN architecture leverages truncated and partially frozen layers from existing models, augmented with novel components such as Auxiliary Fusing Layers (AuxFL), Fusion Residual Block (FuRB), and Alpha Dropouts (αDO) to improve precision and robustness. This design facilitates the precise identification of conditions such as ulcerative colitis, polyps, esophagitis, and healthy colons. Our methodology involved preprocessing endoscopic images sourced from open databases, including KVASIR and ETIS-Larib Polyp DB, using adaptive histogram equalization (AHE) to enhance their quality. The MF-CNN framework supports detailed feature mapping for improved interpretability of the model’s internal workings. An ablation study was conducted to validate the contribution of each component, demonstrating that the integration of AuxFL, αDO, and FuRB played a crucial part in reducing overfitting and efficiency saturation and enhancing overall model performance. The MF-CNN demonstrated outstanding performance in terms of efficacy, achieving an accuracy rate of 99.25%. It also excelled in other key performance metrics with a precision of 99.27%, a recall of 99.25%, and an F1-score of 99.25%. These metrics confirmed the model’s proficiency in accurate classification and its capability to minimize false positives and negatives across all tested GI disease categories. Furthermore, the AUC values were exceptional, averaging 1.00 for both test and validation sets, indicating perfect discriminative ability. The findings of the P-R curve analysis and confusion matrix further confirmed the robust classification performance of the MF-CNN. This research introduces a technique for medical imaging that can potentially transform diagnostics in gastrointestinal healthcare facilities worldwide.

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Detection and Classification of Gastrointestinal Diseases using Machine Learning

Cited by 19 publications

References 86 publications

Secure medical image transmission using deep neural network in e‐health applications

Secure medical image transmission using deep neural network in e‐health applications

Categorizing white blood cells by utilizing deep features of proposed 4B-AdditionNet-based CNN network with ant colony optimization

Development of a multi-fusion convolutional neural network (MF-CNN) for enhanced gastrointestinal disease diagnosis in endoscopy image analysis

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