Use of Convolutional Neural Network for Fish Species Classification

Fish population monitoring systems based on underwater video recording are becoming more popular nowadays, however, manual processing and analysis of such data can be time-consuming. Therefore, by utilizing machine learning algorithms, the data can be processed more efficiently. In this research, authors investigate the possibility of convolutional neural network (CNN) implementation for fish species classification. The dataset used in this research consists of four fish species (Plectroglyphidodon dickii, Chromis chrysura, Amphiprion clarkii, and Chaetodon lunulatus), which gives a total of 12859 fish images. For the aforementioned classification algorithm, different combinations of hyperparameters were examined as well as the impact of different activation functions on the classification performance. As a result, the best CNN classification performance was achieved when Identity activation function is applied to hidden layers, RMSprop is used as a solver with a learning rate of 0.001, and a learning rate decay of 1e-5. Accordingly, the proposed CNN model is capable of performing high-quality fish species classifications

Detection of the Types of Consumable Saltwater Fish in the Coastal Area of Likupang: Uses the Convolutional Neural Network Method

In the coastal area of Likupang, many types of saltwater fish can be consumed, such as tuna and skipjack. Yet, there are also types of saltwater fish that cannot be consumed or protected by the government, such as Napoleon fish and sea kingfish. Thus, this research aimed to build a desktop application that can automatically classify consumable and non-consumable saltwater fish species more accurately and promptly using a suitable image recognition method like the Convolutional Neural Network (CNN). CNN has abilities to distinguish images by recognizing several pixels in a two-dimensional image and RGB (Red, Green, Blue) colors which are then converted into a matrix with various values, making it easier for the system to recognize the two-dimensional image. By using 40% test data (143 images) and 60% training data (213 images), test accuracy in identifying and classifying images of consumable fish, non-consumable fish, and non-fish images with each percentage of 94%, 98%, and 95% respectively.    Di perairan Likupang terdapat banyak jenis ikan air asin yang bisa dikonsumsi, seperti tuna dan cakalang. Namun, ada juga jenis ikan air asin yang tidak boleh dikonsumsi atau dilindungi oleh pemerintah, seperti ikan napoleon dan kingfish laut. Oleh karena itu, pengklasifikasian jenis ikan air asin yang boleh dikonsumsi, ikan tidak boleh dikonsumsi, atau ikan dilindungi di perairan Likupang merupakan pekerjaan yang membutuhkan pengetahuan dan waktu yang lama. Mengetahui banyaknya jenis ikan air asin yang tidak dapat dengan cepat dibedakan antara ikan yang dapat dimakan dan tidak dapat dikonsumsi, maka perlu adanya pendekatan digital untuk mengidentifikasi jenis ikan air asin dengan cepat dan mudah. Metode Convolutional Neural Network (CNN) dapat digunakan untuk membedakan citra dengan mengenali beberapa piksel pada citra dua dimensi dan warna RGB (Merah, Hijau, Biru) yang kemudian diubah menjadi matriks dengan berbagai nilai sehingga menjadikannya memudahkan sistem untuk mengenali gambar dua dimensi. Dengan menggunakan data uji 40% (143 citra) dan 60% data latih (213 citra), penelitian ini memperoleh uji akurasi dalam mengidentifikasi dan mengklasifikasi citra ikan yang dapat dikonsumsi, ikan yang tidak bisa dikonsumsi, dan gambar bukan fish dengan persentase masing-masing sebesar 94%, 98%, dan 95%.&nbsp

Deep Machine Learning for Oral Cancer : From Precise Diagnosis to Precision Medicine

Oral squamous cell carcinoma (OSCC) is one of the most prevalent cancers worldwide and its incidence is on the rise in many populations. The high incidence rate, late diagnosis, and improper treatment planning still form a significant concern. Diagnosis at an early-stage is important for better prognosis, treatment, and survival. Despite the recent improvement in the understanding of the molecular mechanisms, late diagnosis and approach toward precision medicine for OSCC patients remain a challenge. To enhance precision medicine, deep machine learning technique has been touted to enhance early detection, and consequently to reduce cancer-specific mortality and morbidity. This technique has been reported to have made a significant progress in data extraction and analysis of vital information in medical imaging in recent years. Therefore, it has the potential to assist in the early-stage detection of oral squamous cell carcinoma. Furthermore, automated image analysis can assist pathologists and clinicians to make an informed decision regarding cancer patients. This article discusses the technical knowledge and algorithms of deep learning for OSCC. It examines the application of deep learning technology in cancer detection, image classification, segmentation and synthesis, and treatment planning. Finally, we discuss how this technique can assist in precision medicine and the future perspective of deep learning technology in oral squamous cell carcinoma.© 2022 Alabi, Almangush, Elmusrati and Mäkitie. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.fi=vertaisarvioitu|en=peerReviewed

The Right Direction Needed to Develop White-Box Deep Learning in Radiology, Pathology, and Ophthalmology: A Short Review

The popularity of deep learning (DL) in the machine learning community has been dramatically increasing since 2012. The theoretical foundations of DL are well-rooted in the classical neural network (NN). Rule extraction is not a new concept, but was originally devised for a shallow NN. For about the past 30 years, extensive efforts have been made by many researchers to resolve the “black box” problem of trained shallow NNs using rule extraction technology. A rule extraction technology that is well-balanced between accuracy and interpretability has recently been proposed for shallow NNs as a promising means to address this black box problem. Recently, we have been confronting a “new black box” problem caused by highly complex deep NNs (DNNs) generated by DL. In this paper, we first review four rule extraction approaches to resolve the black box problem of DNNs trained by DL in computer vision. Next, we discuss the fundamental limitations and criticisms of current DL approaches in radiology, pathology, and ophthalmology from the black box point of view. We also review the conversion methods from DNNs to decision trees and point out their limitations. Furthermore, we describe a transparent approach for resolving the black box problem of DNNs trained by a deep belief network. Finally, we provide a brief description to realize the transparency of DNNs generated by a convolutional NN and discuss a practical way to realize the transparency of DL in radiology, pathology, and ophthalmology

A deep learning model for drug screening and evaluation in bladder cancer organoids

Three-dimensional cell tissue culture, which produces biological structures termed organoids, has rapidly promoted the progress of biological research, including basic research, drug discovery, and regenerative medicine. However, due to the lack of algorithms and software, analysis of organoid growth is labor intensive and time-consuming. Currently it requires individual measurements using software such as ImageJ, leading to low screening efficiency when used for a high throughput screen. To solve this problem, we developed a bladder cancer organoid culture system, generated microscopic images, and developed a novel automatic image segmentation model, AU2Net (Attention and Cross U2Net). Using a dataset of two hundred images from growing organoids (day1 to day 7) and organoids with or without drug treatment, our model applies deep learning technology for image segmentation. To further improve the accuracy of model prediction, a variety of methods are integrated to improve the model’s specificity, including adding Grouping Cross Merge (GCM) modules at the model’s jump joints to strengthen the model’s feature information. After feature information acquisition, a residual attentional gate (RAG) is added to suppress unnecessary feature propagation and improve the precision of organoids segmentation by establishing rich context-dependent models for local features. Experimental results show that each optimization scheme can significantly improve model performance. The sensitivity, specificity, and F1-Score of the ACU2Net model reached 94.81%, 88.50%, and 91.54% respectively, which exceed those of U-Net, Attention U-Net, and other available network models. Together, this novel ACU2Net model can provide more accurate segmentation results from organoid images and can improve the efficiency of drug screening evaluation using organoids

Prokaryote growth temperature prediction with machine learning

Archaea and bacteria can be divided into four groups based on their growth temperature adaptation: mesophiles, thermophiles, hyperthermophiles, and psychrophiles. The thermostability of proteins is a sum of multiple different physical forces such as van der Waals interactions, chemical polarity, and ionic interactions. Genes causing the adaptation have not been identified and this thesis aims to identify temperature adaptation linked genes and predict temperature adaptation based on the absence or presence of genes. A dataset of 4361 genes from 711 prokaryotes was analyzed with four different machine learning algorithms: neural network, random forest, gradient boosting machine, and logistic regression. Logistic regression was chosen to be an explanatory and predictive model based on micro averaged AUC and Occam’s razor principle. Logistic regression was able to predict temperature adaptation with good performance. Machine learning is a powerful predictor for temperature adaptation and less than 200 genes were needed for the prediction of each adaptation. This technique can be used to predict the adaptation of uncultivated prokaryotes. However, the statistical importance of genes connected to temperature adaptation was not verified and this thesis did not provide much additional support for previously proposed temperature adaptation linked genes

Human Action Recognition for Intelligent Video Surveillance

DissertationCrime remains a persistent threat in South Africa. This has significant implications for our ability to function as a country. As a result, there is a dire need for crime prevention strategies and measures that seek to reduce the risk of crimes occurring, and their potential harmful effects on individuals and society. Many local businesses, organisations and homes utilise video surveillance as a measure, as it can capture the crime as it is committed, thus identifying the perpetrators, or at least presenting a few suspects. In current video surveillance systems, there is no software that enables security officers to manage the data collected (i.e. automatically describe activities occurring in the video) and make it easily accessible for query and investigation. Access to the data is difficult because of the nature and size of the data. There is a need for efficiently extracting data to automatically detect, track, and recognise objects of interest, including understanding and analysing data through intelligent video surveillance. The aim of the study is to create an intelligent vision system that can identify a range of human actions in surveillance videos. This would offer security officers additional data of activities occurring in the videos, thus enabling them to access specific incidents faster and provide early detections of crimes. To achieve this, a literature study was done in the research area to reveal the prerequisites for such systems, the separate software modules designed and developed and eventually integrated into the intended system. Tests were developed to validate the system and evaluate how all the modules work together. This inevitably confirms the functionality of the fundamental components and the system in its entirety. The results have indicated that each module in the system operates successfully, can effectively extract pose estimation features, generate features for training/ classification and classify the features using a deep neural network. Further results showed that capability of the system can be applied to intelligent surveillance systems and enable security officers’ early detection of abnormal behaviour that can lead to crime