Anuj Sharma¹, Deepak Prashar², Arfat Ahmad Khan³, Faizan Ahmed Khan⁴, Settawit Poochaya^3,*

CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 3629-3648, 2022, DOI:10.32604/cmc.2022.030879

Abstract Leukaemia is a type of blood cancer that is caused by undeveloped White Blood Cells (WBC), and it is also called a blast blood cell. In the marrow of human bones, leukaemia is developed and is responsible for blood cell generation with leukocytes and WBC, and if any cell gets blasted, then it may become a cause of death. Therefore, the diagnosis of leukaemia in its early stages helps greatly in the treatment along with saving human lives. Subsequently, in terms of detection, image segmentation techniques play a vital role, and they turn out to be the important image processing… More >

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 >

R. Vidhya^1,*, T. T. Mirnalinee²

Intelligent Automation & Soft Computing, Vol.34, No.2, pp. 911-925, 2022, DOI:10.32604/iasc.2022.025060

Abstract Computer tomography (CT) scan images can provide more helpful diagnosis information regarding the lung cancers. Many machine learning and deep learning algorithms are formulated using CT input scan images for the improvisation in diagnosis and treatment process. But, designing an accurate and intelligent system still remains in darker side of the research side. This paper proposes the novel classification model which works on the principle of fused features and optimized learning network. The proposed framework incorporates the principle of saliency maps as a first tier segmentation, which is then fused with deep convolutional neural networks to improve the classification maps… More >

Hiam Alquran^1,2, Wan Azani Mustafa^3,4,*, Isam Abu Qasmieh², Yasmeen Mohd Yacob^3,4, Mohammed Alsalatie⁵, Yazan Al-Issa⁶, Ali Mohammad Alqudah²

CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 5117-5134, 2022, DOI:10.32604/cmc.2022.025692

Abstract Cervical cancer is screened by pap smear methodology for detection and classification purposes. Pap smear images of the cervical region are employed to detect and classify the abnormality of cervical tissues. In this paper, we proposed the first system that it ables to classify the pap smear images into a seven classes problem. Pap smear images are exploited to design a computer-aided diagnoses system to classify the abnormality in cervical images cells. Automated features that have been extracted using ResNet101 are employed to discriminate seven classes of images in Support Vector Machine (SVM) classifier. The success of this proposed system… More >

Areej A. Malibari¹, Reem Alshahrani², Fahd N. Al-Wesabi^3,*, Siwar Ben Haj Hassine³, Mimouna Abdullah Alkhonaini⁴, Anwer Mustafa Hilal⁵

CMC-Computers, Materials & Continua, Vol.72, No.2, pp. 3799-3813, 2022, DOI:10.32604/cmc.2022.026131

Abstract Medical image processing becomes a hot research topic in healthcare sector for effective decision making and diagnoses of diseases. Magnetic resonance imaging (MRI) is a widely utilized tool for the classification and detection of prostate cancer. Since the manual screening process of prostate cancer is difficult, automated diagnostic methods become essential. This study develops a novel Deep Learning based Prostate Cancer Classification (DTL-PSCC) model using MRI images. The presented DTL-PSCC technique encompasses EfficientNet based feature extractor for the generation of a set of feature vectors. In addition, the fuzzy k-nearest neighbour (FKNN) model is utilized for classification process where the… More >

K. Poongodi^1,*, A. Sabari²

Intelligent Automation & Soft Computing, Vol.33, No.2, pp. 939-953, 2022, DOI:10.32604/iasc.2022.023038

Abstract The microarray gene expression data has a large number of genes with different expression levels. Analyzing and classifying datasets with entire gene space is quite difficult because there are only a few genes that are informative. The identification of bio-marker genes is significant because it improves the diagnosis of cancer disease and personalized medicine is suggested accordingly. Initially, the parallelized minimum redundancy and maximum relevance ensemble (mRMRe) is employed to select top m informative genes. The selected genes are then fed into the Genetic Algorithm (GA) that selects the optimal set of genes heuristically, which uses Mahalanobis Distance (MD) as… More >

Nishant Behar^*, Manish Shrivastava

CMES-Computer Modeling in Engineering & Sciences, Vol.130, No.2, pp. 823-839, 2022, DOI:10.32604/cmes.2022.017030

Abstract Breast cancer is considered an immense threat and one of the leading causes of mortality in females. It is curable only when detected at an early stage. A standard cancer diagnosis approach involves detection of cancer-related anomalies in tumour histopathology images. Detection depends on the accurate identification of the landmarks in the visual artefacts present in the slide images. Researchers are continuously striving to develop automatic machine-learning algorithms for processing medical images to assist in tumour detection. Nowadays, computer-based automated systems play an important role in cancer image analysis and help healthcare experts make rapid and correct inferences about the… More >

Abdul Karim¹, Azhari Azhari^1,*, Mobeen Shahroz², Samir Brahim Belhaouri³, Khabib Mustofa¹

CMC-Computers, Materials & Continua, Vol.71, No.2, pp. 3887-3903, 2022, DOI:10.32604/cmc.2022.021218

Abstract Leukemia is blood cancer, including bone marrow and lymphatic tissues, typically involving white blood cells. Leukemia produces an abnormal amount of white blood cells compared to normal blood. Deoxyribonucleic acid (DNA) microarrays provide reliable medical diagnostic services to help more patients find the proposed treatment for infections. DNA microarrays are also known as biochips that consist of microscopic DNA spots attached to a solid glass surface. Currently, it is difficult to classify cancers using microarray data. Nearly many data mining techniques have failed because of the small sample size, which has become more critical for organizations. However, they are not… More >

Hanan A. Hosni Mahmoud^*, Amal H. Alharbi, Doaa S. Khafga

Intelligent Automation & Soft Computing, Vol.29, No.3, pp. 803-814, 2021, DOI:10.32604/iasc.2021.018607

Abstract In this paper, we aim to apply deep learning convolution neural network (Deep-CNN) technology to classify breast masses in mammograms. We develop a Deep-CNN combined with multi-feature extraction and transfer learning to detect breast cancer. The Deep-CNN is utilized to extract features from mammograms. A support vector machine (SVM) is then trained on the Deep-CNN features to classify normal, benign, and cancer cases. The scoring features from the Deep-CNN are coupled with texture features and used as inputs to the final classifier. Two texture features are included: texture features of spatial dependency and gradient-based histograms. Both are employed to locate… More >