Alia Alshehri, Duaa AlSaeed^*

Intelligent Automation & Soft Computing, Vol.37, No.1, pp. 939-970, 2023, DOI:10.32604/iasc.2023.037096

Abstract One of the most prevalent cancers in women is breast cancer. Early and accurate detection can decrease the mortality rate associated with breast cancer. Governments and health organizations emphasize the significance of early breast cancer screening since it is associated to a greater variety of available treatments and a higher chance of survival. Patients have the best chance of obtaining effective treatment when they are diagnosed early. The detection and diagnosis of breast cancer have involved using various image types and imaging modalities. Breast “infrared thermal” imaging is one of the imaging modalities., a screening instrument used to measure the… More >

Ahmed A. Alsheikhy^1,*, Yahia F. Said¹, Tawfeeq Shawly²

Computer Systems Science and Engineering, Vol.46, No.1, pp. 799-817, 2023, DOI:10.32604/csse.2023.035269

Abstract Lung cancer is the leading cause of cancer-related death around the globe. The treatment and survival rates among lung cancer patients are significantly impacted by early diagnosis. Most diagnostic techniques can identify and classify only one type of lung cancer. It is crucial to close this gap with a system that detects all lung cancer types. This paper proposes an intelligent decision support system for this purpose. This system aims to support the quick and early detection and classification of all lung cancer types and subtypes to improve treatment and save lives. Its algorithm uses a Convolutional Neural Network (CNN)… More >

P. S. Ramapraba^1,*, P. Epsiba², K. Umapathy³, E. Sivanantham⁴

Intelligent Automation & Soft Computing, Vol.36, No.2, pp. 2177-2189, 2023, DOI:10.32604/iasc.2023.032040

Abstract The classification of lung nodules is a challenging problem as the visual analysis of the nodules and non-nodules revealed homogenous textural patterns. In this work, an Auxiliary Classifier (AC)-Generative Adversarial Network (GAN) based Lung Cancer Classification (LCC) system is developed. The proposed AC-GAN-LCC system consists of three modules; preprocessing, Lungs Region Detection (LRD), and AC-GAN classification. A Wiener filter is employed in the preprocessing module to remove the Gaussian noise. In the LRD module, only the lung regions (left and right lungs) are detected using iterative thresholding and morphological operations. In order to extract the lung region only, flood filling… More >

Erkan Akkur¹, Fuat TURK^2,*, Osman Erogul¹

Computer Systems Science and Engineering, Vol.45, No.2, pp. 1017-1031, 2023, DOI:10.32604/csse.2023.033003

Abstract Breast cancer seriously affects many women. If breast cancer is detected at an early stage, it may be cured. This paper proposes a novel classification model based improved machine learning algorithms for diagnosis of breast cancer at its initial stage. It has been used by combining feature selection and Bayesian optimization approaches to build improved machine learning models. Support Vector Machine, K-Nearest Neighbor, Naive Bayes, Ensemble Learning and Decision Tree approaches were used as machine learning algorithms. All experiments were tested on two different datasets, which are Wisconsin Breast Cancer Dataset (WBCD) and Mammographic Breast Cancer Dataset (MBCD). Experiments were… More >

K. Jagadeesh^1,*, A. Rajendran²

Computer Systems Science and Engineering, Vol.45, No.2, pp. 2017-2032, 2023, DOI:10.32604/csse.2023.029169

Abstract Lung Cancer is one of the hazardous diseases that have to be detected in earlier stages for providing better treatment and clinical support to patients. For lung cancer diagnosis, the computed tomography (CT) scan images are to be processed with image processing techniques and effective classification process is required for appropriate cancer diagnosis. In present scenario of medical data processing, the cancer detection process is very time consuming and exactitude. For that, this paper develops an improved model for lung cancer segmentation and classification using genetic algorithm. In the model, the input CT images are pre-processed with the filters called… More >

S. Ayshwarya Lakshmi^1,*, K. Anandavelu²

Intelligent Automation & Soft Computing, Vol.35, No.3, pp. 3403-3413, 2023, DOI:10.32604/iasc.2023.030970

Abstract Skin cancer segmentation is a critical task in a clinical decision support system for skin cancer detection. The suggested enhanced cuckoo search based optimization model will be used to evaluate several metrics in the skin cancer picture segmentation process. Because time and resources are always limited, the proposed enhanced cuckoo search optimization algorithm is one of the most effective strategies for dealing with global optimization difficulties. One of the most significant requirements is to design optimal solutions to optimize their use. There is no particular technique that can answer all optimization issues. The proposed enhanced cuckoo search optimization method indicates… More >

Yassir Edrees Almalki¹, Maida Khalid², Sharifa Khalid Alduraibi³, Qudsia Yousaf², Maryam Zaffar², Shoayea Mohessen Almutiri⁴, Muhammad Irfan⁵, Mohammad Abd Alkhalik Basha⁶, Alaa Khalid Alduraibi³, Abdulrahman Manaa Alamri⁷, Khalaf Alshamrani⁸, Hassan A. Alshamrani^8,*

CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 4103-4121, 2022, DOI:10.32604/cmc.2022.029039

Abstract Since reporting cases of breast cancer are on the rise all over the world. Especially in regions such as Pakistan, Saudi Arabia, and the United States. Efficient methods for the early detection and diagnosis of breast cancer are needed. The usual diagnosis procedures followed by physicians has been updated with modern diagnostic approaches that include computer-aided support for better accuracy. Machine learning based practices has increased the accuracy and efficiency of medical diagnosis, which has helped save lives of many patients. There is much research in the field of medical imaging diagnostics that can be applied to the variety of… More >

Tamer AbuKhalil¹, Bassam A. Y. Alqaralleh^2,*, Ahmad H. Al-Omari³

CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 57-71, 2022, DOI:10.32604/cmc.2022.024367

Abstract Prevention of cervical cancer becomes essential and is carried out by the use of Pap smear images. Pap smear test analysis is laborious and tiresome work performed visually using a cytopathologist. Therefore, automated cervical cancer diagnosis using automated methods are necessary. This paper designs an optimal deep learning based Inception model for cervical cancer diagnosis (ODLIM-CCD) using pap smear images. The proposed ODLIM-CCD technique incorporates median filtering (MF) based pre-processing to discard the noise and Otsu model based segmentation process. Besides, deep convolutional neural network (DCNN) based Inception with Residual Network (ResNet) v2 model is utilized for deriving the feature… More >

José Escorcia-Gutierrez^1,*, Romany F. Mansour², Kelvin Beleño³, Javier Jiménez-Cabas⁴, Meglys Pérez¹, Natasha Madera¹, Kevin Velasquez¹

CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 4221-4235, 2022, DOI:10.32604/cmc.2022.022322

Abstract Biomedical image processing is a hot research topic which helps to majorly assist the disease diagnostic process. At the same time, breast cancer becomes the deadliest disease among women and can be detected by the use of different imaging techniques. Digital mammograms can be used for the earlier identification and diagnostic of breast cancer to minimize the death rate. But the proper identification of breast cancer has mainly relied on the mammography findings and results to increased false positives. For resolving the issues of false positives of breast cancer diagnosis, this paper presents an automated deep learning based breast cancer… 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 >