Mahmoud Ragab^1,2,3,*, Hesham A. Abdushkour⁴, Alaa F. Nahhas⁵, Wajdi H. Aljedaibi⁶

CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 533-546, 2022, DOI:10.32604/cmc.2022.028856

Abstract Lung cancer is the main cause of cancer related death owing to its destructive nature and postponed detection at advanced stages. Early recognition of lung cancer is essential to increase the survival rate of persons and it remains a crucial problem in the healthcare sector. Computer aided diagnosis (CAD) models can be designed to effectually identify and classify the existence of lung cancer using medical images. The recently developed deep learning (DL) models find a way for accurate lung nodule classification process. Therefore, this article presents a deer hunting optimization with deep convolutional neural network for lung cancer detection and… More >

Mohammad Alamgeer¹, Hanan Abdullah Mengash², Radwa Marzouk², Mohamed K Nour³, Anwer Mustafa Hilal^4,*, Abdelwahed Motwakel⁴, Abu Sarwar Zamani⁴, Mohammed Rizwanullah⁴

CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 1437-1448, 2022, DOI:10.32604/cmc.2022.027896

Abstract Early detection of lung cancer can help for improving the survival rate of the patients. Biomedical imaging tools such as computed tomography (CT) image was utilized to the proper identification and positioning of lung cancer. The recently developed deep learning (DL) models can be employed for the effectual identification and classification of diseases. This article introduces novel deep learning enabled CAD technique for lung cancer using biomedical CT image, named DLCADLC-BCT technique. The proposed DLCADLC-BCT technique intends for detecting and classifying lung cancer using CT images. The proposed DLCADLC-BCT technique initially uses gray level co-occurrence matrix (GLCM) model for feature… More >

Yasmeen Al-Saeed^1,2, Wael A. Gab-Allah¹, Hassan Soliman¹, Maysoon F. Abulkhair³, Wafaa M. Shalash⁴, Mohammed Elmogy^1,*

CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 4871-4894, 2022, DOI:10.32604/cmc.2022.023638

Abstract 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… More >

Zhaohui Bu¹, Xuejun Zhang^2,3,*, Jianxiang Lu⁴, Huan Lao⁵, Chan Liang², Xianfu Xu², Yini Wei², Hongjie Zeng²

Molecular & Cellular Biomechanics, Vol.19, No.1, pp. 17-28, 2022, DOI:10.32604/mcb.2022.018318

Abstract The early symptom of lung tumor is always appeared as nodule on CT scans, among which 30% to 40% are malignant according to statistics studies. Therefore, early detection and classification of lung nodules are crucial to the treatment of lung cancer. With the increasing prevalence of lung cancer, large amount of CT images waiting for diagnosis are huge burdens to doctors who may missed or false detect abnormalities due to fatigue. Methods: In this study, we propose a novel lung nodule detection method based on YOLOv3 deep learning algorithm with only one preprocessing step is needed. In order to overcome… More >

Mohamed Ibrahim Waly¹, Mohamed Yacin Sikkandar¹, Mohamed Abdelkader Aboamer¹, Seifedine Kadry², Orawit Thinnukool^3,*

CMC-Computers, Materials & Continua, Vol.70, No.2, pp. 3295-3309, 2022, DOI:10.32604/cmc.2022.020713

Abstract Biomedical imaging is an effective way of examining the internal organ of the human body and its diseases. An important kind of biomedical image is Pap smear image that is widely employed for cervical cancer diagnosis. Cervical cancer is a vital reason for increased women’s mortality rate. Proper screening of pap smear images is essential to assist the earlier identification and diagnostic process of cervical cancer. Computer-aided systems for cancerous cell detection need to be developed using deep learning (DL) approaches. This study introduces an intelligent deep convolutional neural network for cervical cancer detection and classification (IDCNN-CDC) model using biomedical… More >

Thejovathi Murari¹, L. Prathiba², Kranthi Kumar Singamaneni^3,*, D. Venu⁴, Vinay Kumar Nassa⁵, Rachna Kohar⁶, Satyajit Sidheshwar Uparkar⁷

Intelligent Automation & Soft Computing, Vol.31, No.2, pp. 1241-1256, 2022, DOI:10.32604/iasc.2022.020203

Abstract In recent times, big data analytics using Machine Learning (ML) possesses several merits for assimilation and validation of massive quantity of complicated healthcare data. ML models are found to be scalable and flexible over conventional statistical tools, which makes them suitable for risk stratification, diagnosis, classification and survival prediction. In spite of these benefits, the utilization of ML in healthcare sector faces challenges which necessitate massive training data, data preprocessing, model training and parameter optimization based on the clinical problem. To resolve these issues, this paper presents new Big Data Analytics with Optimal Elman Neural network (BDA-OENN) for clinical decision… More >