Abdulqader Mohammed Khalaf, Mohammed Abdel Razek, Mohamed El-dosuky, Ahmed Sobhi

Breast Cancer Detection and Classification Using Deep Learning Techniques Based On Ultrasound Image
Authors: Abdulqader Mohammed Khalaf, Mohammed Abdel Razek, Mohamed El-dosuky, Ahmed Sobhi (BEEI ID 8397)

Breast cancer ranks as the most prevalent form of cancer diagnosed in women, and diagnosis faces several challenges, a change in the size, shape and appearance of breasts, dense breast tissue, lumps or thickening in the breast especially if in only one breast, lumps and nodules in the breast. The major challenge that faces deep learning diagnosis of breast cancer was its shape, size and position non-uniformity especially malignant cancer. We and all researchers always strive through the computer‐aided diagnosis (CAD) system and others to provide assistance in the medical field to detect and classification the type of tumor. In this work proposed a deep learning system for analysis medical image that increased the accuracy of detection and classification of breast cancer types from ultrasound images. It reaches 99.29% accuracy, exceeding other previous work. First, image processing was applied to in enhance the quality of input images. Second, the image segmentation was performed using U-Net architecture. Third, many features are extracted using Mobilenet. Finally, classification using Visual Geometry Group VGG16, the accuracy of detection and classification using for proposed system was evaluated.

Supported by Master Program of Electrical and Computer Engineering, Universitas Ahmad Dahlan, https://mee.uad.ac.id #yogyakarta
Admission: https://mee.uad.ac.id/pendaftaran/

#scopus #journal #publications #publication #uad #electricvehicle #EV #solution #environment #pollution #passenger #fieldorientedcontrolled #FOC #PermanentMagnetSynchronousMotor #PMSM #MATLAB #gradient #truck #regenerative #propulsion #MachineLearning #DeepLearning #InternetOfThings #Classification #CloudComputing #ConvolutionalNeuralNetwork #CNN #SupportVectorMachine #SVM #GeneticAlgorithm #IoT #Security #ArtificialIntelligence #Optimization #COVID-19 #ParticleSwarmOptimization #PSO #ImageProcessing #Clustering #NeuralNetwork #ArtificialNeuralNetwork #FuzzyLogic #RenewableEnergy #5G #WirelessSensorNetwork #WSN #DataMining #Cryptography #Photovoltaic #FeatureSelection #Encryption #Microcontroller #DistributedGeneration #FeatureExtraction #NaturalLanguageProcessing #NLP #TransferLearning #WirelessSensorNetworks #Prediction #SentimentAnalysis #PowerQuality #Simulation #DecisionTree #BigData #RandomForest #Arduino #Sensors #Segmentation #EnergyEfficiency #FPGA #MobileApplication #Algorithm #EnergyConsumption #MATLAB #Blockchain #PIDController #Sensor #Authentication #ComputerVision #THD #TotalHarmonicDistortion #Harmonics #RaspberryPi #FaceRecognition #ImageClassification #IntrusionDetectionSystem #LuminousFlux #QualityOfService #QoS #ElectricVehicle #EV #Network #Routing #SocialMedia #SosMed #Steganography #TextMining #Throughput #Accuracy #AugmentedReality #DeepNeuralNetwork #MANET #MultilevelInverter #NaiveBayes #Performance #Temperature #TextClassification #Elearning #LoadBalancing #NetworkLifetime #PrincipalComponentAnalysis #PCA #Android #BitErrorRate #BER #ColorHomogeneity #Efficiency #Healthcare #MPPT #Microgrid #MiescatteringTheory #RFID #KNN #OFDM #GPS #GSM

Pendaftaran Magister S2 Teknik Elektro, Universitas Ahmad Dahlan: https://mee.uad.ac.id/pendaftaran/

Mekanisme Pendaftaran
1. Mendaftar Online
Pendaftaran online dilakukan di web: pmb-online.uad.ac.id, dengan membuat “akun pendaftaran” terlebih dahulu, kemudian login dengan akun yang sudah dibuat, kemudian pilih formulir Pascasarjana yang sesuai (S2 Teknik Elektro)

2. Isi Formulir Online
Setelah berhasil mengisi dan menyimpan formulir, klik “cetak” untuk mengetahui nomor pendaftaran, silakan di screenshot atau print out hasil pendaftaran. Kemudian scan berkas pendaftaran dalam bentuk pdf atau jpg :
1. Foto Berwarna
2. Ijazah S1 Asli/yang sudah dilegalisir
3. Transkrip S1 Asli/ yang sudah dilegalisir
..
6. Sertifikat TOEFL dengan skor minimal 475

3. Membayar Biaya Pendaftaran
Pembayaran pendaftaran dengan menggunakan nomor pendaftaran, pembayaran dapat dilakukan ke Teller Bank Rekanan UAD atau Via ATM tertentu (cara bayar)

4. Konfirmasi Pendaftaran
Kirimkan Foto slip pembayaran pendaftaran dan Portofolio S2 (download) email : [email protected] dan [email protected] dengan subyek email : “DAFTAR PASCA+JALUR+NO.PENDAFTRAN+NAMA+PRODI PILIHAN”
Contoh: “DAFTAR PASCA +PASCA TEKNIK+202911012+ARIFASANI+ELEKTRO”

7. Registrasi Ulang

8. Konfirmasi Registrasi
Kemudian kirimkan foto SLIP PEMBAYARAN REGISTRASI ke email [email protected] dan [email protected] dengan subyek email : “REGISTRASI PASCA+JALUR+NO.PENDAFTRAN+NAMA+PRODI PILIHAN”


INFORMASI PENDAFTARAN
Sekretariat PMTE UAD Kampus 4

atau dapat menghubungi narahubung pendaftaran mahasiswa baru PMTE UAD di nomor 081392725629 (Vita)