There have been several empirical studies addressing breast cancer using machine learning and soft computing techniques. Many claim that their algorithms are faster, easier, or more accurate than others are. This study is based on genetic programming and machine learning algorithms that aim to construct a system to accurately differentiate between benign and malignant breast tumors. The aim of this study was to optimize the learning algorithm. In this context, we applied the genetic programming technique to select the best features and perfect parameter values of the machine learning classifiers. The performance of the proposed method was based on sensitivity, specificity, precision, accuracy, and the roc curves. The present study proves that genetic programming can automatically find the best model by combining feature preprocessing methods and classifier algorithms.

No abstract

Contrast enhancement is important and plays vital role in many applications. Histogram equalization-based techniques are widely used techniques for contrast enhancement. However, it faces the contrast over-stretching, which in return causes the loss of details and unnatural look to the target image. To address this issue, this work presents a novel scheme for image contrast enhancement. The contribution of the proposed scheme is twofold. First, the image can lose many important information when an image size is decreased. For that, the image is transformed from spatial to wavelet domain so that the multi-resolution can be achieved. Second, Gamma correction is a proven technique that produces natural look and preserves mean brightness of an image with the choice of optimal gamma values. Here, Particle Swarm Optimization (PSO) is utilized to select the optimal gamma values. In this study, an effective fitness function is proposed to maximize the performance of PSO. Experimental findings show that the proposed approach improve the image contrast up to a greater extent without introducing any artifacts.

No abstract

Liver cancer is a life-threatening illness and one of the fastest-growing cancer types in the world. Consequently, the early detection of liver cancer leads to lower mortality rates. This work aims to build a model that will help clinicians determine the type of tumor when it occurs within the liver region by analyzing images of tissue taken from a biopsy of this tumor. Working within this stage requires effort, time, and accumulated experience that must be possessed by a tissue expert to determine whether this tumor is malignant and needs treatment. Thus, a histology expert can make use of this model to obtain an initial diagnosis. This study aims to propose a deep learning model using convolutional neural networks (CNNs), which are able to transfer knowledge from pre-trained global models and decant this knowledge into a single model to help diagnose liver tumors from CT scans. Thus, we obtained a hybrid model capable of detecting CT images of a biopsy of a liver tumor. The best results that we obtained within this research reached an accuracy of 0.995, a precision value of 0.864, and a recall value of 0.979, which are higher than those obtained using other models. It is worth noting that this model was tested on a limited set of data and gave good detection results. This model can be used as an aid to support the decisions of specialists in this field and save their efforts. In addition, it saves the effort and time incurred by the treatment of this type of cancer by specialists, especially during periodic examination campaigns every year.