One of the leading causes of mortality worldwide is liver cancer. The earlier the detection of hepatic tumors, the lower the mortality rate. This paper introduces a computer-aided diagnosis system to extract hepatic tumors from computed tomography scans and classify them into malignant or benign tumors. Segmenting hepatic tumors from computed tomography scans is considered a challenging task due to the fuzziness in the liver pixel range, intensity values overlap between the liver and neighboring organs, high noise from computed tomography scanner, and large variance in tumors shapes. The proposed method consists of three main stages; liver segmentation using Fast Generalized Fuzzy C-Means, tumor segmentation using dynamic thresholding, and the tumor's classification into malignant/benign using support vector machines classifier. The performance of the proposed system was evaluated using three liver benchmark datasets, which are MICCAI-Sliver07, LiTS17, and 3Dircadb. The proposed computer adided diagnosis system achieved an average accuracy of 96.75%, sensetivity of 96.38%, specificity of 95.20% and Dice similarity coefficient of 95.13%.

Background: Liver cancer due to hepatic tumors is one of the primary mortality causes globally. Detecting and diagnosing such tumors can be very tricky in reducing death rates. Segmenting liver tumors from computed tomography (CT) images is a very tricky and challenging task due to many factors such as fuzziness of the liver intensities range, liver pixels intensities values intersection with the neighboring abdomen organs, noise imposed by CT scanner, and variance in tumors and appearances. This paper introduces a Computer-Aided Diagnosis (CAD) system based on some of the Fuzzy C-means (FCM) method variations to detect liver tumors on CT images. Furthermore, one of the main objectives of this paper is to diagnose and label detected liver tumors, either benign or malignant. Methods: Multi-scale Fuzzy C-Means (MSFCM) is used as the liver segmentation method, and its output is the liver segmented out of the abdomen CT image. In order to achieve high-quality liver tumors segmentation, the segmentation is done using two FCM algorithms; Gaussian Kernelized Fuzzy C-Means (GKFCM) and Fast Generalized Fuzzy C-Means (FGFCM), respectively. The diagnosis is achieved by extracting features from segmented tumors and passing them to the support vector machines (SVM) classifier.Results: To evaluate the overall performance of the proposed CAD system, the system was implemented using CT images from three liver benchmark datasets with a total of 250 subjects. The used datasets are MICCAI-Sliver07, LiTS17 and 3Dircadb. Different performance metrics were calculated, such as accuracy (ACC), sensitivity (SEN), specificity (SPE), and dice similarity score (DSC). The proposed system achieved reasonable performance results with an average ACC, SEN, SPE, and DSC of 96.62%, 95.84%,94.20%, and 95.21%, respectively. Conclusions: The proposed system was able to differentiate benign and malignant tumors with reasonable accuracy. The resulted accuracy resulted from our CAD system features such as noise robustness, detail persevering, and being a fully-automatic system with no need for user interaction. The experimental results showed the applicability of the proposed system using different liver datasets.

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Numerous goods and services are now offered through online platforms due to the recent growth of online transactions like e-commerce. Users have trouble locating the product that best suits them from the numerous products available in online shopping. Many studies in deep learning-based recommender systems (RSs) have focused on the intricate relationships between the attributes of users and items. Deep learning techniques have used consumer or item-related traits to improve the quality of personalized recommender systems in many areas, such as tourism, news, and e-commerce. Various companies, primarily e-commerce, utilize sentiment analysis to enhance product quality and effectively navigate today's business environment. Customer feedback regarding a product is gathered through sentiment analysis, which uses contextual data to split it into separate polarities. The explosive rise of the e-commerce industry has resulted in a large body of literature on e-commerce from different perspectives. Researchers have made an effort to categorize the recommended future possibilities for e-commerce study as the field has grown. There are several challenges in e-commerce, such as fake reviews, frequency of user reviews, advertisement click fraud, and code-mixing. In this review, we introduce an overview of the preliminary design for e-commerce. Second, the concept of deep learning, e-commerce, and sentiment analysis are discussed. Third, we represent different versions of the commercial dataset. Finally, we explain various difficulties facing RS and future research directions.

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