A review of convolutional neural network based methods for medical image classification.

This study systematically reviews CNN-based medical image classification methods. We surveyed 149 of the latest and most important papers published to date and conducted an in-depth analysis of the methods used therein. Based on the selected literature, we organized this review systematically.

Acne Detection Based on Reconstructed Hyperspectral Images.

Acne Vulgaris is a common type of skin disease that affects more than 85% of teenagers and frequently continues even in adulthood. While it is not a dangerous skin disease, it can significantly impact the quality of life. Hyperspectral imaging (HSI), which captures a wide spectrum of light, has emerged as a tool for the detection and diagnosis of various skin conditions.

Evaluating Nuclear Membrane Irregularity for the Classification of Cervical Squamous Epithelial Cells.

Pap test involves searching of morphological changes in cervical squamous epithelial cells by pathologists or cytotechnologists to identify potential cancerous cells in the cervix. Nuclear membrane irregularity is one of the morphological changes of malignancy. This paper proposes two novel techniques for the evaluation of nuclear membrane irregularity.

Automated Sperm Head Detection Using Intersecting Cortical Model Optimised by Particle Swarm Optimization.

In human sperm motility analysis, sperm segmentation plays an important role to determine the location of multiple sperms. To ensure an improved segmentation result, the Laplacian of Gaussian filter is implemented as a kernel in a pre-processing step before applying the image segmentation process to automatically segment and detect human spermatozoa. This study proposes an intersecting cortical model (ICM), which was derived from several visual cortex models, to segment the sperm head region.

Automated cervical precancerous cells screening system based on Fourier transform infrared spectroscopy features.

Fourier transform infrared (FTIR) spectroscopy technique can detect the abnormality of a cervical cell that occurs before the morphological change could be observed under the light microscope as employed in conventional techniques. This paper presents developed features extraction for an automated screening system for cervical precancerous cell based on the FTIR spectroscopy as a second opinion to pathologists. The automated system generally consists of the developed features extraction and classification stages.

A Fuzzy-C-Means-Clustering Approach: Quantifying Chromatin Pattern of Non-Neoplastic Cervical Squamous Cells.

Despite the effectiveness of Pap-smear test in reducing the mortality rate due to cervical cancer, the criteria of the reporting standard of the Pap-smear test are mostly qualitative in nature. This study addresses the issue on how to define the criteria in a more quantitative and definite term. A negative Pap-smear test result, i.

Underwater image quality enhancement through composition of dual-intensity images and Rayleigh-stretching.

The quality of underwater image is poor due to the properties of water and its impurities. The properties of water cause attenuation of light travels through the water medium, resulting in low contrast, blur, inhomogeneous lighting, and color diminishing of the underwater images. This paper proposes a method of enhancing the quality of underwater image.

Feature-based ordering algorithm for data presentation of fuzzy ARTMAP ensembles.

This brief presents a new ordering algorithm for data presentation of fuzzy ARTMAP (FAM) ensembles. The proposed ordering algorithm manipulates the presentation order of the training data for each member of a FAM ensemble such that the categories created in each ensemble member are biased toward the vector of the chosen input feature. Diversity is created by varying the training presentation order based on the ascending order of the values from the most uncorrelated input features.

Multiple adaptive neuro-fuzzy inference system with automatic features extraction algorithm for cervical cancer recognition.

To date, cancer of uterine cervix is still a leading cause of cancer-related deaths in women worldwide. The current methods (i.e.

Determination of important topographic factors for landslide mapping analysis using MLP network.

Landslide is one of the natural disasters that occur in Malaysia. Topographic factors such as elevation, slope angle, slope aspect, general curvature, plan curvature, and profile curvature are considered as the main causes of landslides. In order to determine the dominant topographic factors in landslide mapping analysis, a study was conducted and presented in this paper.

Discretized data pattern in endoscopic gastritis images using Dynamic Window and Pairwise Gini Criterion.

Curent standard clinical procedure for gastritis is via endoscopy by performing an invasive procedure. The procedure takes tissue samples from patient's antrum and diagnoses based on pathological evaluation. Several non-invasive computer-aided visualization studies have been conducted to perform feature extraction from the endoscopic gastritis images.

Computer-aided segmentation system for breast MRI tumour using modified automatic seeded region growing (BMRI-MASRG).

In this paper, an automatic computer-aided detection system for breast magnetic resonance imaging (MRI) tumour segmentation will be presented. The study is focused on tumour segmentation using the modified automatic seeded region growing algorithm with a variation of the automated initial seed and threshold selection methodologies. Prior to that, some pre-processing methodologies are involved.

Intelligent medical disease diagnosis using improved hybrid genetic algorithm--multilayer perceptron network.

An improved genetic algorithm procedure is introduced in this work based on the theory of the most highly fit parents (both male and female) are most likely to produce healthiest offspring. It avoids the destruction of near optimal information and promotes further search around the potential region by encouraging the exchange of highly important information among the fittest solution. A novel crossover technique called Segmented Multi-chromosome Crossover is also introduced.

Adaptive evolutionary artificial neural networks for pattern classification.

This paper presents a new evolutionary approach called the hybrid evolutionary artificial neural network (HEANN) for simultaneously evolving an artificial neural networks (ANNs) topology and weights. Evolutionary algorithms (EAs) with strong global search capabilities are likely to provide the most promising region. However, they are less efficient in fine-tuning the search space locally.

Drug-like and non drug-like pattern classification based on simple topology descriptor using hybrid neural network.

An intelligent prediction system has been developed to discriminate drug-like and non drug-like molecules pattern. The system is constructed by using the application of advanced version of standard multilayer perceptron (MLP) neural network called Hybrid Multilayer Perceptron (HMLP) neural network and trained using Modified Recursive Prediction Error (MRPE) training algorithm. In this work, a well understood and easy excess Rule of Five + Veber filter properties are selected as the topological descriptor.

An automated cervical pre-cancerous diagnostic system.

Objective: This paper proposes to develop an automated diagnostic system for cervical pre-cancerous. METHODS AND DATA SAMPLES: The proposed automated diagnostic system consists of two parts; an automatic feature extraction and an intelligent diagnostic. In the automatic feature extraction, the system automatically extracts four cervical cells features (i.