Genetic-Based Multiresolution Noisy Color Image Segmentation

Segmentation of a color image composed of different kinds of regions can be a hard problem, namely to compute for an exact texture fields and make a decision of the optimum number of segmentation areas in an image when it contains similar and/or unstationary texture fields. A local novel neighborhood-based segmentation approach is proposed. Genetic algorithm is used in the proposed limited segment-pass optimization process. In this pass, an energy function, which is defined based on Markov Random Fields, is minimized. The proposed system uses an adaptive threshold estimation method for image thresholding in the wavelet domain based on the Generalized Gaussian Distribution (GGD) modeling of sub band coefficients. This method called Normal Shrink is computationally more efficient and adaptive because the parameters required for estimating the threshold depend on sub band data energy that used in the pre-stage of segmentation. A quadtree is utilized to implement the fast clustering segments for multiresolution framework analysis, which enables the use of different strategies at different resolution levels, and hence, the omputation can be accelerated. The experimental results of the proposed segmentation approach are very encouragin

Deep Learning Approach for Predicting Prostate Cancer from MRI Images

According to medical data, prostate cancer has been one of the most lethal malignancies in recent years. Early detection of prostate cancer significantly influences the tumor's treatability. Image analysis software that operates using a machine learning or deep learning algorithm is one of the techniques utilized to aid in the early and rapid identification of prostate cancer. This paper evaluates the performance of three deep learning Convolutional neural network (CNN) algorithms in detecting prostate cancer. Using Python, three deep learning models, ResNet50, InceptionV3, and VGG16, are subsequently created on the Kaggle platform. These three models have been applied to various medical image diagnostic problems and have won several contests. This study used 620 image samples from the Cancer Imaging Archive (TCIA) data source. Accuracy, f1 score, recall, and precision are used to evaluate the performance of the three models. The extracted test results indicate that the VGG16 achieves the highest level of accuracy at 95.56 percent, followed by the ResNet50 at 86.67 percent and the InceptionV3 at 85.56 percent

Deep Residual Learning Image Recognition Model for Skin Cancer Disease Detection and Classification

Skin cancer is undoubtedly one of the deadliest diseases, and early detection of this disease can save lives. The usefulness and capabilities of deep learning in detecting and categorizing skin cancer based on images have been investigated in many studies. However, due to the variety of skin cancer tumour shapes and colours, deep learning algorithms misclassify whether a tumour is cancerous or benign. In this paper, we employed three different pre-trained state-of-the-art deep learning models: DenseNet121, VGG19 and an improved ResNet152, in classifying a skin image dataset. The dataset has a total of 3297 dermatoscopy images and two diagnostic categories: benign and malignant. The three models are supported by transfer learning and have been tested and evaluated based on the criteria of accuracy, loss, precision, recall, f1 score and ROC. Subsequently, the results show that the improved ResNet152 model significantly outperformed the other models and achieved an accuracy score of 92% and an ROC score of 91%. The DenseNet121 and VGG19 models achieve accuracy scores of 90% and 79% and ROC scores of 88% and 75%, respectively. Subsequently, a deep residual learning skin cancer recognition (ResNetScr) system has been implemented based on the ResNet152 model, and it has the capacity to help dermatologists in diagnosing skin cancer