ABSTRACT
Liver cancer ranks as the sixth most prevalent cancer among all cancers globally. Computed tomography (CT) scanning is a non-invasive analytic imaging sensory system that provides greater insight into human structures than traditional X-rays, which are typically used to make the diagnosis. Often, the final product of a CT scan is a three-dimensional image constructed from a series of interlaced two-dimensional slices. Remember that not all slices deliver useful information for tumor detection. Recently, CT scan images of the liver and its tumors have been segmented using deep learning techniques. The primary goal of this study is to develop a deep learning-based system for automatically segmenting the liver and its tumors from CT scan pictures, and also reduce the amount of time and labor required by speeding up the process of diagnosing liver cancer. At its core, an Encoder-Decoder Network (En-DeNet) uses a deep neural network built on UNet to serve as an encoder, and a pre-trained EfficientNet to serve as a decoder. In order to improve liver segmentation, we developed specialized preprocessing techniques, such as the production of multichannel pictures, de-noising, contrast enhancement, ensemble, and the union of model predictions. Then, we proposed the Gradational modular network (GraMNet), which is a unique and estimated efficient deep learning technique. In GraMNet, smaller networks called SubNets are used to construct larger and more robust networks using a variety of alternative configurations. Only one new SubNet modules is updated for learning at each level. This helps in the optimization of the network and minimizes the amount of computational resources needed for training. The segmentation and classification performance of this study is compared to the Liver Tumor Segmentation Benchmark (LiTS) and 3D Image Rebuilding for Comparison of Algorithms Database (3DIRCADb01). By breaking down the components of deep learning, a state-of-the-art level of performance can be attained in the scenarios used in the evaluation. In comparison to more conventional deep learning architectures, the GraMNets generated here have a low computational difficulty. When associated with the benchmark study methods, the straight forward GraMNet is trained faster, consumes less memory, and processes images more rapidly.
ABSTRACT
In recent years, lung cancer prediction is an essential topic for reducing the death rate of humans. In the literature section, some papers are reviewed that reduce the accuracy level during the prediction stage. Hence, in this paper, we develop a Multi-Process Remora Optimized Hyperparameters of Convolutional Neural Network (MPROH-CNN) aimed at lung cancer prediction. The proposed technique can be utilized to detect the CT images of the human lung. The proposed technique proceeds with four phases, including pre-processing, feature extraction and classification. Initially, the databases are collected from the open-source system. After that, the collected CT images contain unwanted noise, which affects classification efficiency. So, the pre-processing techniques can be considered to remove unwanted noise from the input images, such as filtering and contrast enhancement. Following that, the essential features are extracted with the assistance of feature extraction techniques such as histogram, texture and wavelet. The extracted features are utilized to classification stage. The proposed classifier is a combination of the Remora Optimization Algorithm (ROA) and Convolutional Neural Network (CNN). In the CNN, the ROA is utilized for multi process optimization such as structure optimization and hyperparameter optimization. The proposed methodology is implemented in MATLAB and performances are evaluated by utilized performance matrices such as accuracy, precision, recall, specificity, sensitivity and F_Measure. To validate the projected approach, it is compared with the traditional techniques CNN, CNN-Particle Swarm Optimization (PSO) and CNN-Firefly Algorithm (FA), respectively. From the analysis, the proposed method achieved a 0.98 accuracy level in the lung cancer prediction.
