Salam, rekan Nawala! Semoga kalian selalu dalam keadaan sehat.
Ini adalah Nawala IAES dari Institute of Advanced Engineering and Science. Hari ini kami ingin berbagi wawasan yang bertemakan teknologi deep learning (DL). DL memungkinkan komputer untuk mengenali dan mengekstrak karakteristik visual. Dalam proses mengenali dan mengekstrak karakteristik visual, komputer menggunakan metode dan teknik untuk mencapai akurasi hasil yang diinginkan. Convolutional neural network (CNN) merupakan salah satu metode yang digunakan untuk memproses karakteristik visual. Reddy dan Khanaa, didalam penelitiannya, menggunakan CNN untuk mendeteksi dan mengklasifikasikan kangker paru-paru berdasarkan pengolahan citra. Lebih lengkapnya dapat dibaca pada artikel berikut:
Lung cancer is one of the leading causes of cancer mortality. The overlapping of cancer cells makes early diagnosis difficult. When lung cancer is found early, many therapy choices are reduced, the danger of invasive surgery is reduced, and the chance of survival increases. The primary goal of this study work is to identify early-stage lung cancer and categories using an intelligent deep learning algorithm. Following a thorough review of the literature, we discovered that certain classifiers are ineffective while others are almost perfect. In general, several different kinds of images are employed, but computer tomography scanned images are preferable due to their reduced noise. Intelligent deep learning algorithm is one such approach that employs convolutional neural network techniques and has been shown to be the most effective way for medical image processing, lung nodule identification, classification, feature extraction, and lung cancer prediction. The characteristics are taken from the segmented images and classified using intelligent deep learning algorithm. The suggested techniques’ performances are assessed based on their accuracy, sensitivity, specificity, recall, and precision.
Intelligent deep learning algorithm for lung cancer detection and classification |
N. Sudhir Reddy, V. Khanaa
CNN terus dikembangkan untuk menghasilkan akurasi yang lebih tinggi. Jasim dan Atia memgembangkan block-based CNN untuk mengklasifikasi citra. Hasil yang didapatkan dari metode yang diusulkan dapat meningkatkan akurasi dibandingkan dengan metode CNN yang lain sebesar 3%. Informasi lebih detail dapat dibaca pada artikel berikut:
Image classification is the process of assigning labeling to the input images to a fixed set of categories; however, assigning labels to the image is difficult by using the traditional method because of the large number of images. To solve this problem, we will resort to deep learning techniques. Which is enables computers to recognize and extract visual characteristics. The convolutional neural network (CNN) is a deep neural network used for many purposes, such as image classification, detection, and face recognition, due to its high-performance accuracy in classification and detection tasks. In this paper, we develop CNN based on the transfer learning approach for image classification. The network comprises two types of transfer learning, ResNet and DenseNet, as building blocks of the network with an multilayer perceptron (MLP) classifier. The proposed method does not need to preprocess before these datasets that input into the network. It was train on two datasets: the Cifar-10 and the Sign-Traffic datasets. We conclude that the proposed method achieves the best performance compared with other states of the art. The accuracy gained is 97.45% and 99.45%, respectively, where the proposed CNN increased the accuracy compared to other methods by 3%.
Towards classification of images by using block-based CNN |
Retaj Matroud Jasim, Tayseer Salman Atia
Di dunia medis, pengembangan DL sangatlah diperlukan. Seperti pada penelitian sebelumnya yang membahas mengenai klasifikasi citra kangker paru-paru, Kadhim dan Kamil melakukan pengembangan yang membahas mengenai kangker payudara. Penelitian tersebut mengkombinasikan DL dengan metode penyaringan Gabor untuk meningkatkan akurasi hasil diagnosa kangker payudara. Adapun penjelasan rinci dapat dibaca melalui artikel berikut:
Breast cancer is the most common type of cancer in women and the leading cause of death from a malignant growth in the world. Machine learning methods have been created to help with cancer detection accuracy. There are several methods for detecting cancer. Histopathological images are more accurate. In this study, we employed the Gabor filter to extract statistical features from invasive ductal carcinoma histopathology images. From the histopathological images, we chose 100, 200, 400, 1000, and 2000 at random. These statistical features were used to train several models to classify these images as malignant or benign, including the decision tree, quadratic discriminant analysis, extra randomized trees, gradient boosting, Gaussian process, Naive Bayes, nearest centroid, multilayer perceptron, and support vector machine. The models’ accuracy, sensitivity, specificity, precision, and F1_score were examined. The models produced the highest results when there were 100 images and a wavenumber of 0.2. While as the number of images increased, the models’ effectiveness reduced. The most obvious finding to emerge from this study is that we suggest using deep learning instead of machine learning models for large datasets.
Breast invasive ductal carcinoma diagnosis using machine learning models and Gabor filter method of histology images |
Rania R. Kadhim, Mohammed Y. Kamil
Selain medis, DL juga merambah ke dunia psikologi. Penelitian yang dilakukan oleh Agarwal dkk., DL diimplementasikan untuk mengklasifikasikan emosi manusia. Data yang diklasifikasikan bukanlah data gambar melainkan data musik yang didengarkan. Hasil dari klasifikasi tersebut dapat mengidentifikasi emosi yang dirasakan oleh pendengar musik tersebut, seperti dramatis, bahagia, agresif, sedih, dan romantis. Lebih jelasnya mengenai penelitian tersebut dapat dibaca selengkapnya melalui tautan berikut:
This research is done based on the identification and thorough analyzing musical data that is extracted by the various method. This extracted information can be utilized in the deep learning algorithm to identify the emotion, based on the hidden features of the dataset. Deep learning-based convolutional neural network (CNN) and long short-term memory-gated recurrent unit (LSTM-GRU) models were developed to predict the information from the musical information. The musical dataset is extracted using the fast Fourier transform (FFT) models. The three deep learning models were developed in this work the first model was based on the information of extracted information such as zero-crossing rate, and spectral roll-off. Another model was developed on the information of Mel frequencybased cepstral coefficient (MFCC) features, the deep and wide CNN algorithm with LSTM-GRU bidirectional model was developed. The third model was developed on the extracted information from Mel-spectrographs and untied these graphs based on two-dimensional (2D) data information to the 2D CNN model alongside LSTM models. Proposed model performance on the information from Mel-spectrographs is compared on the F1 score, precision, and classification report of the models. Which shows better accuracy with improved F1 and recall values as compared with existing approaches.
Emotion classification for musical data using deep learning techniques |
Gaurav Agarwal, Sachi Gupta, Shivani Agarwal, Atul Kumar Rai
Beberapa artikel diatas merupakan bagian kecil dari penelitian mengenai pengembangan deep learning. Untuk mendapatkan informasi lebih lanjut silahkan kunjungi secara GRATIS pada link berikut: https://www.beei.org/ dan https://ijres.iaescore.com/.
Redaksi: I. Busthomi