Computerized detection of human diseases is an emerging research domain for the last two decades [1,2]. In medical imaging, numerous remarkable methods were developed for automated medical diagnosis systems [3]. Stomach infection is one of the most common types that caused a large number of deaths every year [4]. The most common type of disease related to the stomach is colorectal cancer. It affects both women and men [5]. Colorectal cancer consists of three significant infections that are polyp, ulcer, and bleeding. In 2015, about 132,000 cases of colorectal cancer were recorded in the USA [6]. A survey conducted in 2017 showed that approximately 21% population of the USA is suffering from gastrointestinal infections, and 765,000 deaths are noticed due to maladies found in the stomach [7]. According to the global cancer report of 2018 for 20 regions of the world, total estimated cases of cancer are 18.1 million. Among them, 9.2% for deaths and 6.1% for new cases relate to colorectal cancer. According to the American cancer society, in 2019, approximately 27,510 new stomach cancer cases of both genders (consisting of 10,280 women’s and 17,230 men’s) are observed in the US. A total of 11,140 deaths (consisting of 4340 women’s and 6800 men’s) are noticed in 2019 due to colorectal cancer [8].

GI infections can be easily cured if they were diagnosed at an early stage. As small bowel has a complex structure, that is why push gastroscopy is not considered as the best choice for the diagnosis of small bowel infections like bleeding, polyp, and ulcer [9]. The traditional endoscopic method is an invasive method that is not utilized by endoscopists and is also not recommended to patients due to its high level of discomfort [10]. These problems were resolved by a new technology introduced in the year 2000, namely, Wireless Capsule Endoscopy (WCE) [11]. WCE is a small pill shaped device, consisting of batteries, a camera, and a light source [12]. While passing through the gastrointestinal tract (GIT), WCE captures almost 50,000 images and releases them through the anus. Mostly malignant diseases of GIT like bleeding, polyp, and ulcer are diagnosed through WCE. WCE is proved to be an authentic modality for painless investigation and examination of GIT [13]. This technique is more convenient to use than traditional endoscopies. Moreover, it provides better diagnostic accuracy for bleeding and tumor detection, specifically in the small intestine [14]. It is too difficult for an expert physician to thoroughly examine all of the captured images as it is a chaotic and time taking task. The manual analysis of WCE frames is not an easy process, and it takes much time [15]. To resolve this problem, researchers are working on introducing several computer-aided diagnosis (CAD) methods [16]. The suggested methods will help the doctors to detect the disease more accurately in less time. A typical CAD system consists of five major steps that are image pre-processing, feature extraction, optimal feature selection, feature fusion, and classification. The extraction of useful features is one of the most significant tasks. Numerous features are extracted in the CAD systems for accurate diagnosis of disease. These features include texture [17], color [18], and so on [19]. All of the extracted features are not useful; there may exist some irrelevant. Therefore, it is essential to reduce the feature vector to remove irrelevant features for better efficiency.

In this work, to detect and classify GI abnormalities, two pipeline procedures are considered: bleeding segmentation and GI abnormality classification. The significant contributions of the work are: i) in the bleeding detection step, a hybrid technique is implemented. In this method, the original image is split into three channels and thresholding is applied for each color channel. After that, pixel-by-pixel matching is performed, and a mask is generated for each channel. Finally, combining all mask images in one image for final bleeding segmentation; ii) in the classification procedure, transfer learning is ustilized for extracting deep learning features. The original images are enhanced by using a unified method, which is a combination of chrominance weight map, gamma correction, haze reduction, and YCbCr color conversion. Later, deep learning features are extracted by using a pre-trained CNN model. Further, the LBP features are also obtained for textural information of each abnormal regions; iii) a new method named entropy controlled ensemble learning is proposed and it selects the best learning features for correct prediction as well as fast execution. The selected features are ensemble in one vector by using a concatenation approach; iv) the performance of the proposed method is validated by several combinations of features. Further, many classification methods are also used for validation of selected features vector.

Several machine learning and computer vision-based techniques are introduced for the diagnosis of human diseases like lung cancer, brain tumor, GIT infections from WCE images, and so on [20,21]. The stomach is one of the most significant organs of the human body. The most conspicuous diseases of the stomach are ulcer, bleeding, and polyp. In the study [22], the authors have utilized six features of different color spaces for the classification of non-ulcer and ulcerous regions. The used color spaces are CMYK, YUV, RGB, HSV, LAB, and XYZ. After the extraction of features, the cross-correlation method is utilized for the fusion of extracted features. Finally, 97.89% accuracy is obtained by utilizing the support vector machine (SVM) for the classification. Suman et al. [23] proposed a new method for the classification of bleeding images from non-bleeding ones. Their suggested method is mainly based on the statistical color features obtained from the RGB images. Charfi et al. [24] presented another methodology for colon irregularity detection from WCE images utilizing variance, LBP, and DWT features. They have been used a multilayer perceptron (MLP) and SVM for the classification. The suggested method performed well than existing methods and achieved 85.86% accuracy on linear SVM and 89.43% accuracy on MLP. In [25], authors have proposed a CAD method for bleeding classification. They have used unsupervised and supervised learning algorithms. Souaidi et al. [26] proposed a unique approach named multiscale complete LBP (MS-CLBP) for ulcer detection which is mainly based on the Laplacian pyramid and completed LBP (CLBP). In this method, ulcer detection is performed using two-color spaces (YCbCr and RGB). They have used the G channel of RGB and Cr of YCbCr for the detection of ulcers. Classification is performed using SVM and attained an average accuracy of 93.88% and 95.11% for both datasets. According to the survey conducted by Fan et al. [27], different pre-trained models of deep learning have covered numerous aspects of the medical imaging domain. Many researchers have utilized CNN models for the accurate classification and segmentation of disease or infections.

In contrast, the images that have the same category should share the same learned features. The overall achieved recognition accuracy of this method is 98%. Sharif et al. [28] have used the contrast-enhanced color features for the segmentation of the infected region from the image. After that, geometric features are pull-out from the resultant segmented portion of the image. Two deep CNN models VGG19 and VGG16, are also used in this method. Extracted deep features of both models are fused using the Euclidean fisher vector (EFV) that are later combined with the geometric characteristics to obtain strong features. Conditional entropy is employed on the resultant feature vector for optimal feature selection, which is classified using the KNN classifier and achieved the highest accuracy of 99.42%. Diamantis et al. [29] have proposed a novel method named as LB-FCN (Look Behind Fully Convolutional Neural Network). The proposed method has performed well than existing methods and achieved better GI abnormality detection results. Alaskar et al. [30] have utilized GoogleNet and AlexNet for the ulcer and non-ulcer images classification. Khan et al. [31] have suggested a technique for the ulcer detection and GI abnormality classification. ResNet101 is utilized by the authors for features extraction. Moreover, they optimized features by utilizing distance fitness function along with grasshopper optimization. They have utilized C-SVM and achieved 99.13% classification accuracy. The literature depicts that CAD systems for disease recognition mostly rely on handcrafted features (shape, color, and texture information). However, impressed by the performance of CNN in other domains, some researchers have employed CNN models in the medical field for disease segmentation and classification [32,33]. Inspired by these studies, we utilized deep learning for better classification accuracy.

A hybrid architecture is proposed in this work for automated detection and classification of stomach abnormalities. The proposed architecture follows two pipeline procedures: Bleeding abnormality segmentation and GI infections classification. The proposed bleeding segmentation procedure is illustrated in Fig. 1. First, select the RGB bleeding images from the Database, then, extract all three channels and apply thresholding. Output images produced by a threshold function are compared by pixel-wise and used for generating a mask for each channel. Later, combined the mask of all three channels, as a result, a segmented image is obtained. The detail description of each step given as follows.

Two datasets are used in this work for the assessment of suggested GI infections detection and classification method. The description of each dataset is given as:

KVASIR Dataset contains a total of 4000 images, which are confirmed by expert doctors [36]. Eight different types of infections are included in this dataset, such as Dyed-Lifted-Polyp (DLP), Dyed-Resection-Margin (DRM), Esophagitis (ESO), Normal-Cecum (NC), Normal-Pylorus (NP), Normal Z-Line (NZL), Polyps (P), and Ulcerative-Colities (UCE). Every class contains 500 images of different resolution-720×576 up to 1920×1072 pixels. Sample images are shown in Fig. 8a.

Private Dataset was collected from COMSATS Computer Vision Lab [37], and it includes a total of 2326 clinical sample images. These images consist of three categories-ulcer, bleeding, and healthy. The image size is 512×512. Some sample images are presented in Fig. 8b.

In this article, we proposed a deep learning architecture for the detection and classification of GI abnormalities. The proposed architecture consists of two procedures for pipeline detection and classification. In the detection task, the bleeding region is segmented by a fusion of three separate channels. In the classification task, deep learning features and texture-oriented features are extracted and the best features are selected using the Shanon Entropy controlled ESDA classifier. The selected features are concatenated and are classified. In the evaluation phase, the segmentation process achieves an average accuracy of over 87% for abnormal bleeding regions. For classification, the accuracy of the private data set is 99.80 percent, while for the Kvasir data set, the accuracy is 87.80 percent. It is concluded from the results that the proposed selection method shows better performance compared to the existing techniques. It also concludes that the merger process is effective for more classes, such as the Kvasir dataset classification. In addition, texture features also have a high impact on disease classification and deep learning fusion, addressing the issue of texture variation. In future studies, we will focus on ulcer segmentation through deep learning.