Search

Background and Objective. One of the illnesses with most significant mortality and morbidity rates worldwide is lung cancer. From CT images, automatic lung tumor segmentation is significantly essential. However, segmentation has several difficulties, such as different sizes, variable shapes, and complex surrounding tissues. Therefore, a novel enhanced combined intelligent system is presented to predict lung cancer in this research. Methods. Non-small cell lung cancer should be recognized for detecting lung cancer. In the pre-processing stage, the noise in the CT images is eliminated by using an average filter and adaptive median filter, and histogram equalization is used to enhance the filtered images to enhance the lung image quality in the proposed model. The adapted deep belief network (ADBN) is used to segment the affected region with the help of network layers from the noise-removed lung CT image. Two cascaded RBMs are used for the segmentation process in the structure of ADBN, including Bernoulli-Bernoulli (BB) and Gaussian-Bernoulli (GB), and then relevant significant features are extracted. The hybrid spiral optimization intelligent-generalized rough set (SOI-GRS) approach is used to select compelling features of the CT image. Then, an optimized light gradient boosting machine (LightGBM) model using the Ensemble Harris hawk optimization (EHHO) algorithm is used for lung cancer classification. Results. LUNA 16, the Kaggle Data Science Bowl (KDSB), the Cancer Imaging Archive (CIA), and local datasets are used to train and test the proposed approach. Python and several well-known modules, including TensorFlow and Scikit-Learn, are used for the extensive experiment analysis. The proposed research accurately spot people with lung cancer according to the results. The method produced the least classification error possible while maintaining 99.87% accuracy. Conclusion. The integrated intelligent system (ADBN-Optimized LightGBM) gives the best results among all input prediction models, taking performance criteria into account and boosting the system's effectiveness, hence enabling better lung cancer patient diagnosis by physicians and radiologists. [ABSTRACT FROM AUTHOR]

The leukemia represents a blood cancer that begins from bone marrow and spread to bloodstream and other limbs. The leukocytes pose various morphologies and size that makes it complex to detect and segment exactly. To consider this issue, this article develops a novel and efficient method for chronic lymphocytic leukemia (CLL) detection with blood smear images. Here, the preprocessing is done with Type 2 fuzzy and cuckoo search‐based filter for discarding artifacts and noise contained in the images. The cell segmentation is done with deep fuzzy clustering for producing the segments from the preprocessed blood smear images. The discovery of CLL is done with random multimodal deep learning (RMDL). The RMDL training is done with proposed Jaya‐competitive swarm optimization (Jaya‐CSO) algorithm. The developed Jaya‐CSO is devised by combining Jaya optimization and competitive swarm optimizer (CSO). Hence, the proposed Jaya‐CSO‐based RMDL is employed for discovering lymphocytic leukemia. The proposed Jaya‐CSO‐based RMDL offered improved performance with highest accuracy of 95.1%, highest true negative rate of 95.4% and highest true positive rate of 95.2%. [ABSTRACT FROM AUTHOR]

Soft computing is an associate rising field that plays a crucial half in the area of engineering and science. One of the most significant applications of soft computing is image segmentation. It focuses on an exploiting tolerance of imprecision and uncertainty. Segmentation supported soft computing remains a difficult task within the medical field. Medical images are habitually used in the segmentation process to extract the meaningful portions and to know and clarify the condition of the particular patient. In this article, we implement an efficient possibilistic fuzzy C‐means (PFCM) approach to segment the lung portion in the computed tomography (CT) image and the result shows that it improves the segmentation accuracy upto 98.5012% and results are compared with existing segmenting approaches like fuzzy possibilistic C‐means method, fuzzy bitplane method and so forth. Also, the PFCM approach increases the diagnostic accuracy of the computer aided diagnosis system using CT images. The radiologist may utilize this computer aided diagnosis system results as a second opinion of their diagnosed results. [ABSTRACT FROM AUTHOR]

Cardiotocography (CTG) is a simultaneous recording of fetal heart rate (FHR) and uterine contractions (UC). It is one of the most common diagnostic techniques to evaluate maternal and fetal well-being during pregnancy and before delivery. By observing the Cardiotocography trace patterns doctors can understand the state of the fetus. There are several signal processing and computer programming based techniques for interpreting a typical Cardiotocography data. Even few decades after the introduction of Cardiotocography into clinical practice, the predictive capacity of the methods remains controversial and still inaccurate. In this paper, we evaluate commonly used unsupervised clustering method Fuzzy C-mean clustering for their suitability towards clustering CTG data. We used Precision, Recall, F-Score and Rand Index as the metric to evaluate the performance. In previous work, the overall Precision, Recall and F-Score were only considered. But in this evaluation, we are going to measure class-wise Precision, Recall and F-Score to make the analysis very specific. The arrived results prove that, even though the traditional clustering methods can identify the Normal CTG patterns, they were incapable of Suspicious and Pathologic patterns. This fact was not highlighted in the previous work. [ABSTRACT FROM AUTHOR]