PENGKLASIFIKASI BERAS MENGGUNAKAN METODE CNN (CONVOLUTIONAL NEURAL NETWORK)

Pemanfaatan Computer Vision yang digunakan untuk pengolahan gambar dan deep learning dalam melakukan proses pembelajaran. Berdasarkan citra image (gambar) beras yang di input, memungkinkan system untuk mengklasifikasikan jenis objek beras. Kemudian, proses deep learning seperti Convolutional Neural Network (CNN). melakukan training pembelajaran dalam memproses gambar yang menjadi data set, dengan melakukan proses learning dalam beberapa jaringan (Network) tersembunyi dan menjadikan gambar yang ingin diuji dapat diklasifikasikan berdasarkan tingkat atau level dari learning (pembelajaran) network itu sendiri. Berdasarkan hasil proses diperoleh, pengklasifikasian beras dengan data yang diambil berupa gambar beras dapat dilakukan dengan melakukan training objek beras yang diujikan. Pengklasifikasian beras dilakukan dengan membedakan 8 (dua) kelas jenis beras yang diambil yaitu beras yang baik dan beras yang jelek. Dengan Metode CNN dapat dihasilkan tingkat keakuratan sampai diatas 90 persen. Sistem pengklasifikasian ini digunakan untuk industri, perusahaan atau stakeholder untuk dimanfaatkan dalam melihat kualitas beras secara lebih cepat, akurat, dan objectif.    Kata Kunci : Dataset beras, beras, CNN, Deep Learnin

Image Embedding of PMU Data for Deep Learning towards Transient Disturbance Classification

This paper presents a study on power grid disturbance classification by Deep Learning (DL). A real synchrophasor set composing of three different types of disturbance events from the Frequency Monitoring Network (FNET) is used. An image embedding technique called Gramian Angular Field is applied to transform each time series of event data to a two-dimensional image for learning. Two main DL algorithms, i.e. CNN (Convolutional Neural Network) and RNN (Recurrent Neural Network) are tested and compared with two widely used data mining tools, the Support Vector Machine and Decision Tree. The test results demonstrate the superiority of the both DL algorithms over other methods in the application of power system transient disturbance classification.Comment: An updated version of this manuscript has been accepted by the 2018 IEEE International Conference on Energy Internet (ICEI), Beijing, Chin

Hierarchical fracture classification of proximal femur X-Ray images using a multistage Deep Learning approach

6noPurpose - Suspected fractures are among the most common reasons for patients to visit emergency departments and often can be difficult to detect and analyze them on film scans. Therefore, we aimed to design a Deep Learning-based tool able to help doctors in diagnosis of bone fractures, following the hierarchical classification proposed by the Arbeitsgemeinschaft für Osteosynthesefragen (AO) Foundation and the Orthopaedic Trauma Association (OTA). Methods - 2453 manually annotated images of proximal femur were used for the classification in different fracture types (1133 Unbroken femur, 570 type A, 750 type B). Secondly, the A type fractures were further classified into the types A1, A2, A3. Two approaches were implemented: the first is a fine-tuned InceptionV3 convolutional neural network (CNN), used as a baseline for our own proposed approach; the second is a multistage architecture composed by successive CNNs in cascade, perfectly suited to the hierarchical structure of the AO/OTA classification. Gradient Class Activation Maps (Grad-CAM) where used to visualize the most relevant areas of the images for classification. The averaged ability of the CNN was measured with accuracy, area under receiver operating characteristics curve (AUC), recall, precision and F1-score. The averaged ability of the orthopedists with and without the help of the CNN was measured with accuracy and Cohen’s Kappa coefficient. Results: We obtained an averaged accuracy of 0.86 (CI 0.84-0.88) for three classes classification and 0.81 (CI 0.79-0.82) for five classes classification. The average accuracy improvement of specialists was 14% with and without the CAD (Computer Assisted Diagnosis) system. Conclusion: We showed the potential of using a CAD system based on CNN for improving diagnosis accuracy and for helping students with a lower level of expertise. We started our work with proximal femur fractures and we aim to extend it to all bone segments further in the future, in order to implement a tool that could be used in every-day hospital routine.partially_openembargoed_20211023Tanzi, Leonardo; Vezzetti, Enrico; Moreno, Rodrigo; Aprato, Alessandro; Audisio, Andrea; Massè, AlessandroTanzi, Leonardo; Vezzetti, Enrico; Moreno, Rodrigo; Aprato, Alessandro; Audisio, Andrea; Massè, Alessandr

DETEKSI TINGKAT KEMANISAN BUAH SEMANGKA (CITRULLUS LANATUS) BERDASARKAN CIRI KULIT BUAH DENGAN MENGGUNAKAN METODE CNN (CONVOLUTIONAL NEURAL NETWORK)

Buah semangka memiliki kandungan gula yang cukup tinggi sehingga bisa menjadi sumber energi bagi tubuh. Namun, kandungan gula pada buah semangka dapat berbeda-beda tergantung pada jenis, ukuran, dan seberapa matang buah tersebut. Salah satu cara yang dapat dilakukan untuk mendapatkan semangka yang manis adalah memperhatikan bagian buah yang terletak diatas tanah (ground spot), bagian ini akan berubah warna dari yang semula putih menjadi kekuningan. Tanda kuning tersebut akan menunjukan semangka matang saat masih di pohon dan dipanen pada saat sudah matang. Dataset diambil dari 197 buah semangka yang telah difoto dari sisi atas, samping dan bawah/sisi lainnya. Dataset diklasifikasikan menjadi 3 kelas, yaitu : manis, cukup manis dan kurang manis. Digunakan Refractometer untuk mengukur tingkat kemanisan buah semangka berdasarkan °Brix. Penelitian ini menggunakan Deep Learning Convolutional Neural Network (CNN) dengan arsitektur EfficientNetV2S serta menggunakan teknik Transfer Learning yang kemudian dilakukan Fine-Tuning. Digunakan 3 buah input gambar semangka yaitu bagian atas (tangkai), samping (ground spot) dan bawah/sisi lainnya. Hasil dari ektraksi fitur di klasifikasi menggunakan CNN untuk menentukan prediksi semangka yang manis (≥ 8 °Brix) cukup manis (≥ 6 s.d 8 °Brix)  dan Kurang Manis (≥4 s.d 6°Brix). Setelah pengujian di dapatkan akurasi sebesar 0.96 dengan perbandingan data training dan data testing 80:20

Predicting survival from colorectal cancer histology slides using deep learning: A retrospective multicenter study

BACKGROUND: For virtually every patient with colorectal cancer (CRC), hematoxylin-eosin (HE)-stained tissue slides are available. These images contain quantitative information, which is not routinely used to objectively extract prognostic biomarkers. In the present study, we investigated whether deep convolutional neural networks (CNNs) can extract prognosticators directly from these widely available images. METHODS AND FINDINGS: We hand-delineated single-tissue regions in 86 CRC tissue slides, yielding more than 100,000 HE image patches, and used these to train a CNN by transfer learning, reaching a nine-class accuracy of >94% in an independent data set of 7,180 images from 25 CRC patients. With this tool, we performed automated tissue decomposition of representative multitissue HE images from 862 HE slides in 500 stage I-IV CRC patients in the The Cancer Genome Atlas (TCGA) cohort, a large international multicenter collection of CRC tissue. Based on the output neuron activations in the CNN, we calculated a "deep stroma score," which was an independent prognostic factor for overall survival (OS) in a multivariable Cox proportional hazard model (hazard ratio [HR] with 95% confidence interval [CI]: 1.99 [1.27-3.12], p = 0.0028), while in the same cohort, manual quantification of stromal areas and a gene expression signature of cancer-associated fibroblasts (CAFs) were only prognostic in specific tumor stages. We validated these findings in an independent cohort of 409 stage I-IV CRC patients from the "Darmkrebs: Chancen der Verhütung durch Screening" (DACHS) study who were recruited between 2003 and 2007 in multiple institutions in Germany. Again, the score was an independent prognostic factor for OS (HR 1.63 [1.14-2.33], p = 0.008), CRC-specific OS (HR 2.29 [1.5-3.48], p = 0.0004), and relapse-free survival (RFS; HR 1.92 [1.34-2.76], p = 0.0004). A prospective validation is required before this biomarker can be implemented in clinical workflows. CONCLUSIONS: In our retrospective study, we show that a CNN can assess the human tumor microenvironment and predict prognosis directly from histopathological images

Pattern matching of footwear Impressions

One of the most frequently secured types of evidence at crime scenes are footware impressions. Identifying the brand and model of the footware can be crucial to narrowing the search for suspects. This is done by forensic experts by comparing the evidence found at the crime scene with a huge list of reference impressions. In order to support the forensic experts an automatic retrieval of the most likely matches is desired.In this thesis different techniques are evaluated to recognize and match footwear impressions, using reference and real crime scene shoeprint images. Due to the conditions in which the shoeprints are found (partial occlusions, variation in shape) a translation, rotation and scale invariant system is needed. A VLAD (Vector of Locally Aggregated Descriptors) encoder is used to clustering descriptors obtained using different approaches, such as SIFT (Scale-Invariant Feature Transform), Dense SIFT in a Triplet CNN (Convolutional Neural Network). These last two approaches provide the best performance results when the parameters are correctly adjusted, using the Cumulative Matching Characteristic curve to evaluate it.En esta tesis se evalúan diferentes técnicas para reconocer y emparejar impresiones de calzado, utilizando imágenes de referencia y de escenas reales de crimen. Debido a las condiciones en que se encuentran las impresiones (oclusiones parciales, variaciones de forma) se necesita un sistema invariante ante translación, rotación y escalado. Para ello se utiliza un codificador VLAD (Vector of Locally Aggregated Descriptors) para agrupar descriptores obtenidos en diferentes enfoques, como SIFT (Scale-Invariant Feature Transform), Dense SIFT y Triplet CNN (Convolutional Neural Network). Estos dos últimos enfoques proporcionan los mejores resultados una vez los parámetros se han ajustado correctamente, utilizando la curva CMC (Characteristic Matching Curve) para realizar la evaluación.En aquesta tesi s'avaluen diferents tècniques per reconèixer i aparellar impressions de calçat, utilitzant imatges de referència i d'escenes reals de crim. Degut a les condicions en què es troben les impressions (oclusions parcials, variació de forma ) es necessita un sistema invariant davant translació, rotació i escalat. Per això s'utilitza un codificador VLAD (Vector of Locally Aggregated Descriptors) per agrupar descriptors obtinguts en diferents enfocaments, com SIFT (Scale-Invariant Feature Transform), Dense SIFT i Triplet CNN (Convolutional Neural Network). Aquests dos últims enfocaments proporcionen els millors resultats un cop els paràmetres s'han ajustat correctament, utilitzant la corba CMC (Characteristic Matching Curve) per realitzar l'avaluació