P. Gomathi^1,*, C. Muniraj², P. S. Periasamy³

Computer Systems Science and Engineering, Vol.45, No.2, pp. 1887-1899, 2023, DOI:10.32604/csse.2023.028808

Abstract The mortality rate decreases as the early detection of Breast Cancer (BC) methods are emerging very fast, and when the starting stage of BC is detected, it is curable. The early detection of the disease depends on the image processing techniques, and it is used to identify the disease easily and accurately, especially the micro calcifications are visible on mammography when they are 0.1 mm or bigger, and cancer cells are about 0.03 mm, which is crucial for identifying in the BC area. To achieve this micro calcification in the BC images, it is necessary to focus on the four… More >

Sukhendra Singh¹, Sur Singh Rawat, Manoj Gupta³, B. K. Tripathi⁴, Faisal Alanazi⁵, Arnab Majumdar⁶, Pattaraporn Khuwuthyakorn⁷, Orawit Thinnukool^7,*

CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 3063-3083, 2023, DOI:10.32604/cmc.2023.032363

Abstract Currently, breast cancer has been a major cause of deaths in women worldwide and the World Health Organization (WHO) has confirmed this. The severity of this disease can be minimized to the large extend, if it is diagnosed properly at an early stage of the disease. Therefore, the proper treatment of a patient having cancer can be processed in better way, if it can be diagnosed properly as early as possible using the better algorithms. Moreover, it has been currently observed that the deep neural networks have delivered remarkable performance for detecting cancer in histopathological images of breast tissues. To… More >

Nishant Behar^*, Manish Shrivastava

CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 3371-3389, 2023, DOI:10.32604/cmc.2023.031949

Abstract Breast cancer is the most prevalent cancer among women, and diagnosing it early is vital for successful treatment. The examination of images captured during biopsies plays an important role in determining whether a patient has cancer or not. However, the stochastic patterns, varying intensities of colors, and the large sizes of these images make it challenging to identify and mark malignant regions in them. Against this backdrop, this study proposes an approach to the pixel categorization based on the genetic algorithm (GA) and principal component analysis (PCA). The spatial features of the images were extracted using various filters, and the… More >

Mahmoud Ragab^1,2,3,*, Maged Mostafa Mahmoud^4,5,6, Amer H. Asseri^2,7, Hani Choudhry^2,7, Haitham A. Yacoub⁸

CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 3279-3295, 2023, DOI:10.32604/cmc.2023.031037

Abstract Colorectal carcinoma (CRC) is one such dispersed cancer globally and also prominent one in causing cancer-based death. Conventionally, pathologists execute CRC diagnosis through visible scrutinizing under the microscope the resected tissue samples, stained and fixed through Haematoxylin and Eosin (H&E). The advancement of graphical processing systems has resulted in high potentiality for deep learning (DL) techniques in interpretating visual anatomy from high resolution medical images. This study develops a slime mould algorithm with deep transfer learning enabled colorectal cancer detection and classification (SMADTL-CCDC) algorithm. The presented SMADTL-CCDC technique intends to appropriately recognize the occurrence of colorectal cancer. To accomplish this,… More >

NUR AIN ALIAS¹, WAN AZANI MUSTAFA^1,2,*, MOHD AMINUDIN JAMLOS³, AHMED ALKHAYYAT⁴, KHAIRUL SHAKIR AB RAHMAN⁵, RAMI Q. MALIK⁶

Oncology Research, Vol.29, No.5, pp. 365-376, 2021, DOI:10.32604/or.2022.025897

Abstract Cervical cancer is a prevalent and deadly cancer that affects women all over the world. It affects about 0.5 million women anually and results in over 0.3 million fatalities. Diagnosis of this cancer was previously done manually, which could result in false positives or negatives. The researchers are still contemplating how to detect cervical cancer automatically and how to evaluate Pap smear images. Hence, this paper has reviewed several detection methods from the previous researches that has been done before. This paper reviews pre-processing, detection method framework for nucleus detection, and analysis performance of the method selected. There are four… More >

P. Saranya^*, P. Asha

Intelligent Automation & Soft Computing, Vol.36, No.1, pp. 1137-1149, 2023, DOI:10.32604/iasc.2023.031470

Abstract In today’s growing modern world environment, as human food activities are changing, it is affecting human health, thus leading to diseases like cancer. Cancer is a complex disease with many subtypes that affect human health without premature treatment and cause death. So the analysis of early diagnosis and prognosis of cancer studies can improve clinical management by analyzing various features of observation, which has become necessary to classify the type in cancer research. The research needs importance to organize the risk of the cancer patients based on data analysis to predict the result of premature treatment. This paper introduces a… More >

Iftikhar Naseer^1,*, Tehreem Masood¹, Sheeraz Akram¹, Arfan Jaffar¹, Muhammad Rashid², Muhammad Amjad Iqbal³

CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 2039-2054, 2023, DOI:10.32604/cmc.2023.032927

Abstract Lung cancer is the most dangerous and death-causing disease indicated by the presence of pulmonary nodules in the lung. It is mostly caused by the instinctive growth of cells in the lung. Lung nodule detection has a significant role in detecting and screening lung cancer in Computed tomography (CT) scan images. Early detection plays an important role in the survival rate and treatment of lung cancer patients. Moreover, pulmonary nodule classification techniques based on the convolutional neural network can be used for the accurate and efficient detection of lung cancer. This work proposed an automatic nodule detection method in CT… More >

Sindhuja Manickavasagam^1,*, Poonkuzhali Sugumaran²

Computer Systems Science and Engineering, Vol.45, No.1, pp. 939-953, 2023, DOI:10.32604/csse.2023.030491

Abstract The combination of machine learning (ML) approaches in healthcare is a massive advantage designed at curing illness of millions of persons. Several efforts are used by researchers for detecting and providing primary phase insights as to cancer analysis. Lung cancer remained the essential source of disease connected mortality for both men as well as women and their frequency was increasing around the world. Lung disease is the unrestrained progress of irregular cells which begin off in one or both Lungs. The previous detection of cancer is not simpler procedure however if it can be detected, it can be curable, also… More >

Ali Hanafiah Rambe¹, Muzammil Jusoh^2,3, Samir Salem Al-Bawri^4,5,*, Mahmoud A. Abdelghany^6,7

CMC-Computers, Materials & Continua, Vol.73, No.3, pp. 5575-5587, 2022, DOI:10.32604/cmc.2022.030902

Abstract This paper proposed integrating the communication system on the garment, which can be utilized to detect breast cancer at an early stage by using an ultra-wideband (UWB) wearable antenna. Breast cancer is an abnormal cell that is located in the breast tissue. Early detection of breast cancer plays an important role, and it helps in the long term for all women. The proposed UWB wearable antenna successfully operates at 3.1–10.6 GHz under an acceptable reflection coefficient of −10 dB. The fabricated wearable antenna was made from Shieldit Super and felt both conductive and nonconductive wearable materials. Few measurement studies of… More >

Nithya Rekha Sivakumar^1,*, Sara Abdelwahab Ghorashi¹, Faten Khalid Karim¹, Eatedal Alabdulkreem¹, Amal Al-Rasheed²

CMC-Computers, Materials & Continua, Vol.73, No.3, pp. 6253-6267, 2022, DOI:10.32604/cmc.2022.029266

Abstract Mobile clouds are the most common medium for aggregating, storing, and analyzing data from the medical Internet of Things (MIoT). It is employed to monitor a patient’s essential health signs for earlier disease diagnosis and prediction. Among the various disease, skin cancer was the wide variety of cancer, as well as enhances the endurance rate. In recent years, many skin cancer classification systems using machine and deep learning models have been developed for classifying skin tumors, including malignant melanoma (MM) and other skin cancers. However, accurate cancer detection was not performed with minimum time consumption. In order to address these… More >