CLASSIFICATION OF CHEST RADIOGRAPHS USING NOVEL ANOMALOUS SALIENCY MAP AND DEEP CONVOLUTIONAL NEURAL NETWORK

The rapid advancement in pattern recognition via the deep learning method has made it possible to develop an autonomous medical image classification system. This system has proven robust and accurate in classifying most pathological features found in a medical image, such as airspace opacity, mass, and broken bone. Conventionally, this system takes routine medical images with minimum pre-processing as the model's input; in this research, we investigate if saliency maps can be an alternative model input. Recent research has shown that saliency maps' application increases deep learning model performance in image classification, object localization, and segmentation. However, conventional bottom-up saliency map algorithms regularly failed to localize salient or pathological anomalies in medical images. This failure is because most medical images are homogenous, lacking color, and contrast variant. Therefore, we also introduce the Xenafas algorithm in this paper. The algorithm creates a new kind of anomalous saliency map called the Intensity Probability Mapping and Weighted Intensity Probability Mapping. We tested the proposed saliency maps on five deep learning models based on common convolutional neural network architecture. The result of this experiment showed that using the proposed saliency map over regular radiograph chest images increases the sensitivity of most models in identifying images with air space opacities. Using the Grad-CAM algorithm, we showed how the proposed saliency map shifted the model attention to the relevant region in chest radiograph images. While in the qualitative study, it was found that the proposed saliency map regularly highlights anomalous features, including foreign objects and cardiomegaly. However, it is inconsistent in highlighting masses and nodules. ABSTRAK: Perkembangan pesat sistem pengecaman corak menggunakan kaedah pembelajaran mendalam membolehkan penghasilan sistem klasifikasi gambar perubatan secara automatik. Sistem ini berupaya menilai secara tepat jika terdapat tanda-tanda patologi di dalam gambar perubatan seperti kelegapan ruang udara, jisim dan tulang patah. Kebiasaannya, sistem ini akan mengambil gambar perubatan dengan pra-pemprosesan minimum sebagai input. Kajian ini adalah tentang potensi peta salien dapat dijadikan sebagai model input alternatif. Ini kerana kajian terkini telah menunjukkan penggunaan peta salien dapat meningkatkan prestasi model pembelajaran mendalam dalam pengklasifikasian gambar, pengesanan objek, dan segmentasi gambar. Walau bagaimanapun, sistem konvensional algoritma peta salien jenis bawah-ke-atas kebiasaannya gagal mengesan salien atau anomali patologi dalam gambar-gambar perubatan. Kegagalan ini disebabkan oleh sifat gambar perubatan yang homogen, kurang variasi warna dan kontras. Oleh itu, kajian ini memperkenalkan algoritma Xenafas yang menghasilkan dua jenis pemetaan saliensi anomali iaitu Pemetaan Kebarangkalian Keamatan dan Pemetaan Kebarangkalian Keamatan Pemberat. Kajian dibuat pada peta salien yang dicadangkan iaitu pada lima model pembelajaran mendalam berdasarkan seni bina rangkaian neural konvolusi yang sama. Dapatan kajian menunjukkan dengan menggunakan peta salien atas gambar-gambar radiografi dada tetap membantu kesensitifan kebanyakan model dalam mengidentifikasi gambar-gambar dengan kelegapan ruang udara. Dengan menggunakan algoritma Grad-CAM, peta salien yang dicadangkan ini mampu mengalih fokus model kepada kawasan yang relevan kepada gambar radiografi dada. Sementara itu, kajian kualitatif ini juga menunjukkan algoritma yang dicadangkan mampu memberi ciri anomali, termasuk objek asing dan kardiomegali. Walau bagaimanapun, ianya tidak konsisten dalam menjelaskan berat dan nodul.

Forest Fire Probability Mapping in Eastern Serbia: Logistic Regression versus Random Forest Method

Forest fire risk has increased globally during the previous decades. The Mediterranean region is traditionally the most at risk in Europe, but continental countries like Serbia have experienced significant economic and ecological losses due to forest fires. To prevent damage to forests and infrastructure, alongside other societal losses, it is necessary to create an effective protection system against fire, which minimizes the harmful effects. Forest fire probability mapping, as one of the basic tools in risk management, allows the allocation of resources for fire suppression, within a fire season, from zones with a lower risk to those under higher threat. Logistic regression (LR) has been used as a standard procedure in forest fire probability mapping, but in the last decade, machine learning methods such as fandom forest (RF) have become more frequent. The main goals in this study were to (i) determine the main explanatory variables for forest fire occurrence for both models, LR and RF, and (ii) map the probability of forest fire occurrence in Eastern Serbia based on LR and RF. The most important variable was drought code, followed by different anthropogenic features depending on the type of the model. The RF models demonstrated better overall predictive ability than LR models. The map produced may increase firefighting efficiency due to the early detection of forest fire and enable resources to be allocated in the eastern part of Serbia, which covers more than one-third of the country’s area.